The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networks, Transformer-based, and Foundation Models. Despite this progress, critical challenges regarding the nature of similarity assessment and model transparency have been neglected. A primary concern is the Geometric Gap, where traditional pairwise measures fail to capture the intrinsic geometry of the dataset manifold. Furthermore, the Interpretability Gap persists, as representations often lack alignment with human cognition. Therefore, how to provide interpretability to representations while maintaining low dimensionality and high effectiveness in downstream tasks remains an open challenge. In this paper, we propose a novel unsupervised framework that integrates Manifold Learning strategies with Rank-based Interpretable Graph Embeddings. Our approach effectively bridges these gaps by first characterizing the contextual information of the dataset through manifold analysis and subsequently generating sparse, self-explainable embeddings. The proposed approach employs a flexible formulation, allowing different Manifold Learning and Representation Learning strategies. Extensive experimental evaluation across diverse datasets and features demonstrates that our Context-Aware representations not only provide intrinsic interpretability and dimensionality reduction but also maintain or enhance effectiveness in downstream tasks, specifically in image retrieval and semi-supervised classification using Graph Convolutional Networks (GCNs).

In a data-driven world, efficiently organizing and mapping relationships between objects is crucial. Graphs are powerful tools for modeling these connections, being widely used in social networks, telecommunications, and biology. However, graph-based methods often face high computational costs, particularly in memory and space usage. To address this, graph embedding techniques, also referred to as Network Representation Learning, encode graph information into lower-dimensional representations while preserving structural aspects. Traditional methods, however, lack interpretable dimensions. RaDE (Rank Diffusion Embedding) introduces a new approach using rank-based information, with a key step being the selection of a representative subset of nodes to provide interpretability for its dimensions and improve retrieval tasks. Despite its potential, RaDE’s original proposal did not fully explore the effectiveness of representative subset selection across different classes or evaluate embeddings in tasks like classification and clustering. Inspired by RaDE, this work introduces GRaCE (Graph and Rank-based Contextual Embeddings), a fully unsupervised framework that generates interpretable embeddings by leveraging robust rank-based measures for representative subset selection and node embedding. GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.

In recent years, various regression methods have been studied in the literature. Although these methods have shown success in different applications, there is no consensus on which one is the best. Different regressors can produce significantly different prediction results when applied to datasets with varying properties. In this paper, we propose Fusion Regression (FuR), a novel approach that combines the predictions of multiple regressors to leverage their complementary views. FuR concatenates the predictions of regressors to create a new feature space and employs a re-ranking scheme for improved accuracy. Our experiments, conducted on 10 datasets with varying properties (such as size and dimension), show that FuR leads to performance gains of up to 20% compared to the best baseline regressor and up to 16% compared to the recently proposed Regression by Re-ranking method.

The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data-independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on seven public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided.

The evolution of image acquisition and storage technologies has been fundamental in numerous medical fields, supporting doctors to deliver more precise diagnoses and, consequently, recommend more effective treatments for their patients. Recently, deep learning techniques have played a key role in more accurate medical image analysis, mainly due to the capacity to effectively represent the image visual content. However, in spite of tremendous advances, deep-learning techniques commonly require huge quantities of data for training, that are not available in many scenarios, especially in the medical domain. Conversely, manifold learning techniques have been successfully applied in unsupervised and semi-supervised scenarios for more effective encoding of similarity relationships between multimedia data in the absence or restriction of labeled data. In this work, we propose to exploit jointly the representation power of deep-learning strategies with the ability of unsupervised manifold learning in delivering more effective similarity measurement. Convolutional Neural Networks (CNNs) and Transformer-based models trained through transfer learning are combined by unsupervised manifold learning methods, which define a more effective similarity among images. The output can be used for unsupervised retrieval and semi-supervised classification based on a k-NN strategy. An experimental evaluation was conducted on different datasets of MRI brain tumor images, considering different features. Effective results were obtained on both retrieval and classification tasks, with significant gains obtained by manifold learning approaches. In scenarios with limited training data, our approach achieves results that are competitive or superior to state-of-the-art deep learning approaches.

The crop yield prediction is crucial for the sugarcane grower to estimate the amount of biomass that will be harvested in decision-making for the acquisition of agricultural fertilizers and pesticides, for carrying out the harvest, and for the reform of the cane field. Usually, the features used for crop yield prediction are based on the direct observations of what occurs on the field collected by sensors or manually. But modeling the problem with new features, calculated by regressions applied to features collected from the phenomenon, can help to explore better the results that dataset retrieves. And it is possible by using these retrieves as new features to be modeled in other regressions. This article explores the viability of producing new features, called here meta-features (MF), to find better results for the sugarcane crop yield prediction. These meta-features were created from the results obtained by different regressors used to analyze which of them would present the best prediction in the original dataset. The regressions using these meta-features obtained better results in terms of ${\bar R^2}$ and errors associated with the crop yield measured on the field.

The representation of multimedia content experienced tremendous advances in the last decades. Mainly supported by deep learning models, impressive results have been obtained. However, despite such advances in representation, the definition of similarity has been neglected. Effectively computing the similarity between representations remains a challenge. Traditional distance functions, such as the Euclidean distance, are not able to properly consider the relevant similarity information encoded in the dataset manifold. In fact, manifolds are essential to perception in many scenarios, such that exploiting the underlying structure of dataset manifolds plays a central role in multimedia content understanding and retrieval. In this paper, we present a framework for unsupervised distance learning which provides easy and uniform access to methods capable of considering the dataset manifold for redefining similarity. Such methods perform context-sensitive similarity learning based on more global measures, capable of improving the effectiveness of retrieval and machine learning tasks. The framework can use distance, similarity, or ranking information both as input and output and compute traditional retrieval effectiveness measures. Implemented as a wrapper in Python, the framework allows integration with a large number of Python libraries while keeping a back-end in C++ for efficiency. The paper also discusses diverse applications of the methods available in the pyUDLF framework, including image re-ranking, video retrieval, person re-ID, and pre-processing of distance measurements for clustering and classification.

In spite of the huge advances in supervised learning, the common requirement for extensive labeled datasets represents a severe bottleneck. In this scenario, other learning paradigms capable of addressing the challenge associated with the scarcity of labeled data represent a relevant alternative solution. This paper presents a novel clustering method called Self-Supervised Graph Convolutional Clustering (SGCC)1, which aims to exploit the strengths of different learning paradigms, combining unsupervised, semi-supervised, and self-supervised perspectives. An unsupervised manifold learning algorithm based on hypergraphs and ranking information is used to provide more effective and global similarity information. The hypergraph structures allow identifying representative items for each cluster, which are used to derive a set of small but high-confident clusters. Such clusters are taken as soft-labels for training a Graph Convolutional Network (GCN) in a semi-supervised classification task. Once trained in a self-supervised setting, the GCN is used to predict the cluster of remaining items. The proposed SGCC method was evaluated both in image and citation networks datasets and compared with classic and recent clustering methods, obtaining high-effective results in all scenarios.

Several approaches based on regression have been developed in the past few years with the goal of improving prediction results, including the use of ranking strategies. Re-ranking has been exploited and successfully employed in several applications, improving rankings by encoding the manifold structure and redefining distances among elements from a dataset. Despite the promising results observed, re-ranking has not been evaluated in regressions tasks . This paper proposes a novel, generic, and customizable framework entitled Regression by Re-ranking (RbR) , which explores the ability of re-ranking algorithms in determining relevant rankings of objects in prediction tasks. The framework relies on the integration of a base regressor , unsupervised re-ranking learning techniques, and predictions associated with nearest neighbours weighted according to their ranking positions. The RbR framework was evaluated under a rigorous experimental protocol and presented significant results in improving the prediction when compared to state-of-the-art approaches.

Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. Experimental results were conducted on 10 different collections. Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios.

Despite the impressive advances obtained by supervised deep learning approaches on retrieval and classification tasks, how to acquire labeled data for training remains a challenging bottleneck. In this scenario, the need for developing more effective content-based retrieval approaches capable of taking advantage of multimodal information and advances in unsupervised learning becomes imperative. Based on such observations, we propose two novel approaches that combine Graph Convolutional Networks (GCNs) with rank-based manifold learning methods. The GCN models were trained in an unsupervised way, using the Deep Graph Infomax algorithm, and the proposed approaches employ recent rank-based manifold learning methods. Multimodal information is exploited through pre-trained CNNs via transfer learning for extracting audio, image, and video features. The proposed approaches were evaluated on three public action recognition datasets. High-effective results were obtained, reaching relative gains up to +29.44% of MAP compared to baseline approaches without GCNs. The experimental evaluation also considered classical and recent baselines in the literature.

Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real applications becomes challenging due to the large labeled collections required during training. Several works have pursued solutions to overcome it by proposing strategies that can learn more for less, e.g., weakly and semi-supervised learning approaches. As these approaches do not usually address memorization and sensitivity to adversarial examples, this paper presents three deep metric learning approaches combined with Mixup for incomplete-supervision scenarios. We show that some state-of-the-art approaches in metric learning might not work well in such scenarios. Moreover, the proposed approaches outperform most of them in different datasets.

Effectively measuring similarity among data samples represented as points in high-dimensional spaces remains a major challenge in retrieval, machine learning, and computer vision. In these scenarios, unsupervised manifold learning techniques grounded on rank information have been demonstrated to be a promising solution. However, various methods rely on rank correlation measures, which often depend on a proper definition of neighborhood size. On current approaches, this definition may lead to a reduction in the final desired effectiveness. In this work, a novel rank correlation measure robust to such variations is proposed for manifold learning approaches. The proposed measure is suitable for diverse scenarios and is validated on a Manifold Learning Algorithm based on Correlation Graph (CG). The experimental evaluation considered 6 datasets on general image retrieval and person Re-ID, achieving results superior to most state-of-the-art methods.

Image and multimedia retrieval has established as a prominent task in an increasingly digital and visual world. Mainly supported by decades of development on hand-crafted features and the success of deep learning techniques, various different feature extraction and retrieval approaches are currently available. However, the frequent requirements for large training sets still remain as a fundamental bottleneck, especially in real-world and large-scale scenarios. In the scarcity or absence of labeled data, choosing what retrieval approach to use became a central challenge. A promising strategy consists in to estimate the effectiveness of ranked lists without requiring any groundtruth data. Most of the existing measures exploit statistical analysis of the ranked lists and measure the reciprocity among lists of images in the top positions. This work innovates by proposing a new and self-supervised method for this task, the Deep Rank Noise Estimator (DRNE). An algorithm is presented for generating synthetic ranked list data, which is modeled as images and provided for training a Convolutional Neural Network that we propose for effectiveness estimation. The proposed model is a variant of the DnCNN (Denoiser CNN), which intends to interpret the incorrectness of a ranked list as noise, which is learned by the network. Our approach was evaluated on 5 public image datasets and different tasks, including general image retrieval and person re-ID. We also exploited and evaluated the complementary between the proposed approach and related rank-based approaches through fusion strategies. The experimental results showed that the proposed method is capable of achieving up to 0.88 of Pearson correlation with MAP measure in general retrieval scenarios and 0.74 in person re-ID scenarios.

Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data.

Summary Despite the continuous advances in image retrieval technologies, performing effective and efficient content‐based searches remains a challenging task. Unsupervised iterative re‐ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re‐ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re‐ranking algorithms creates the need for exploiting efficiency vs effectiveness trade‐offs. In this article, we introduce a class of unsupervised iterative re‐ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL‐Sim, Contextual Re‐ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×, 16.1×, 3.3×, and 7.1× for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems.

Despite the major advances on feature development for low and mid-level representations, a single visual feature is often insufficient to achieve effective retrieval results in different scenarios. Since diverse visual properties provide distinct and often complementary information for a same query, the combination of different features, including handcrafted and learned features, has been establishing as a relevant trend in image retrieval. An intrinsic difficulty task consists in selecting and combining features that provide a high-effective result, which is often supported by supervised learning methods. However, in the absence of labeled data, selecting and fusing features in a completely unsupervised fashion becomes an essential, although very challenging task. The proposed genetic algorithm employs effectiveness estimation measures as fitness functions, making the evolutionary process fully unsupervised. Our approach was evaluated considering 3 public datasets and 35 different descriptors achieving relative gains up to +53.96% in scenarios with more than 8 billion possible combinations of rankers. The framework was also compared to different baselines, including state-of-the-art methods.

Accurately ranking images and multimedia objects are of paramount relevance in many retrieval and learning tasks. Manifold learning methods have been investigated for ranking mainly due to their capacity of taking into account the intrinsic global manifold structure. In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks. Different from traditional graph-based approaches, which represent only pairwise relationships, hypergraphs are capable of modeling similarity relationships among a set of objects. The proposed approach uses the hyperedges for constructing a contextual representation of data samples and exploits the encoded information for deriving a more effective similarity function. An extensive experimental evaluation was conducted on nine public datasets including diverse retrieval scenarios and multimedia content. Experimental results demonstrate that high effectiveness gains can be obtained in comparison with the state-of-the-art methods.

Appropriate metrics are paramount for machine learning and pattern recognition. In Content-based Image Retrieval-oriented applications, low-level features and pairwise-distance metrics are usually not capable of representing similarity among the objects as observed by humans. Therefore, metric learning from available data has become crucial in such applications, but just a few related approaches take into account the contextual information inherent from the samples for a better accuracy performance. In this paper, we propose a novel approach which combines an unsupervised manifold learning algorithm with the Optimum-Path Forest (OPF) classifier to obtain more accurate recognition rates, as well as we show it can outperform standard OPF-based classifiers that are trained over the original manifold. Experiments conducted in some public datasets evidenced the validity of metric learning in the context of OPF classifiers.

Well drilling monitoring is an essential task to prevent faults, save resources, and take care of environmental and eco-planning businesses. During drilling, it is required that staff fill out a log to keep track of the activities that are currently occurring. With such data analyzed and processed, it is possible to learn how to prevent faults and take corrective actions in realtime. However, the most important information is usually stored in a free-text format, thus complicating the task of automated text mining. In this work, we introduce the Optimum-Path Forest (OPF) for sentence classification in drilling reports and compare its results against some state-of-art results. We show that OPF combined with text-based features are a compelling source to learn patterns in drilling reports.

Due to the growing availability of unlabeled data and the difficulties in obtaining labeled data, the use of semi-supervised learning approaches becomes even more promising. The capacity of taking into account the dataset structure is of crucial relevance for effectively considering the unlabeled data. In this paper, a novel classifier is proposed through a manifold learning approach. The graph is constructed based on a new hybrid similarity measure which encodes both supervised and unsupervised information. Next, strongly connected components are computed and used to analyze the dataset manifold. The classification is performed through a voting scheme based on primary (labeled) and secondary (unlabeled) voters. An experimental evaluation is conducted, considering various datasets, diverse situations of training/test dataset sizes and comparison with baselines. The proposed method achieved positive results in most of situations.

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k -Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top- k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets.

Due to the increasing availability of image and multimedia collections, unsupervised post-processing methods, which are capable of improving the effectiveness of retrieval results without the need of user intervention, have become indispensable. This paper presents the Unsupervised Distance Learning Framework (UDLF), a software which enables an easy use and evaluation of unsupervised learning methods. The framework defines a broad model, allowing the implementation of different unsupervised methods and supporting diverse file formats for input and output. Seven different unsupervised methods are initially available in the framework. Executions and experiments can be easily defined by setting a configuration file. The framework also includes the evaluation of the retrieval results exporting visual output results, computing effectiveness and efficiency measures. The source-code is public available, such that anyone can freely access, use, change, and share the software under the terms of the GPLv2 license.

Summary Re‐ranking algorithms have been proposed to improve the effectiveness of content‐based image retrieval systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re‐Ranking (CSRR). One of our approaches consists in parallelizing the algorithm with OpenCL to use the central and graphics processing units of an accelerated processing unit. The other is to modify the algorithm to a version that, when compared with the original CSRR, not only reduces the total running time of our implementations by a median of 1.6 × but also increases the accuracy score in most of our test cases. Combining both parallelization and algorithm modification results in a median speedup of 5.4 × from the original serial CSRR to the parallelized modified version. Different implementations for CSRR's Re‐sort Ranked Lists step were explored as well, providing insights into graphics processing unit sorting, the performance impact of image descriptors, and the trade‐offs between effectiveness and efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

Initial stages of Alzheimer's disease are easily confused with the normal aging process. Additionally, the methodology involved in the diagnosis by radiologists can be subjective and difficult to document. In this scenario, the development of accessible approaches capable of supporting the early diagnosis of Alzheimer's disease is crucial. Various approaches have been employed with this objective, specially using brain MRI scans. Although certain satisfactory accuracy results have been achieved, most of the approaches requires very specific pre-processing steps based on the brain anatomy. In this paper, we present a novel image retrieval approach for supporting the Alzheimer's disease diagnostic, based on general use features and unsupervised post-processing step. The brain MRI scans are processed and retrieved through general features without any pre-processing step. In the following, a rankbased unsupervised distance learning procedure is performed for improving the effectiveness of the initial results. Experimental results demonstrate that the proposed approach can achieve effective retrieval results, being suitable in aiding the diagnosis of Alzheimer's disease.

This paper presents an efficient diffusion-based re-ranking approach. The proposed method propagates contextual information defined in terms of top-ranked objects of ranked lists in a diffusion process. That makes the method suitable for large scale real-world collections. Experiments were conducted considering public image collections, several descriptors, and comparisons with state-of-the-art methods. Experimental results demonstrate that the proposed method provides high effectiveness gains with low computational costs.

Ranking accurately collection images is the main objective of Content-based Image Retrieval (CBIR) systems. In fact, the set of images ranked at the first positions generally defines the effectiveness of provided search services, i.e., they are used for assessing automatically the quality of search systems as this set usually contains the collection images that are of interest. Recently, the use of ranking information (e.g., rank correlation) has been used in different research initiatives with the objective of improving the effectiveness of image retrieval tasks. This paper presents a broad rank correlation analysis for unsupervised distance learning on image retrieval tasks. Various well-known rank correlation measures are considered and two new measures are proposed. Several experiments were conducted considering various image datasets involving shape, color, and texture descriptors. Experimental results demonstrate that ranking information can be exploited for distance learning tasks successfully. Evaluated approaches yield better results in terms of effectiveness than various state-of-the-art algorithms.

Various unsupervised learning methods have been proposed with significant improvements in the effectiveness of image search systems. However, despite the relevant effectiveness gains, these approaches commonly require high computation efforts, not addressing properly efficiency and scalability requirements. In this paper, we present a novel unsupervised learning approach for improving the effectiveness of image retrieval tasks. The proposed method is also scalable and efficient as it exploits parallel and heterogeneous computing on CPU and GPU devices. Extensive experiments were conducted considering five different public image collections and several descriptors. This rigorous experimental protocol evaluates the effectiveness, efficiency, and scalability of the proposed approach, and compares it with previous methods. Experimental results demonstrate that high effectiveness gains (up to +29%) can be obtained requiring small run times.

This paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach yields better results in terms of effectiveness than various methods recently proposed in the literature.

This paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distance defines a more effective distance between two images, and is used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach is also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of proposed approach. The Reciprocal kNN Distance yields better results in terms of effectiveness than various state-of-the-art algorithms.

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

This work is developed in the context of the placing task of the MediaEval 2011 initiative. The objective is to geocode (or geotag) a set of videos, i.e., automatically assign geographical coordinates to them. This paper presents an architecture for multimodal geocoding that exploits both visual and textual descriptions associated with videos. This work also describes our efforts regarding the implementation of this architecture to demonstrate its applicability. Conducted experiments show how our multimodal approach enhances the results compared to relying on a single modality.

The huge growth of image collections and multimedia resources available is remarkable. One of the most common approaches to support image searches relies on the use of Content-Based Image Retrieval (CBIR) systems. CBIR systems aim at retrieving the most similar images in a collection, given a query image. Since the effectiveness of those systems is very dependent on the accuracy of ranking approaches, re-ranking algorithms have been proposed to exploit contextual information and improve the effectiveness of CBIR systems. Image re-ranking algorithms typically consider the relationship among every image in a given dataset when computing the new ranking. This approach demands a huge amount of computational power, which may render it prohibitive on very large data sets. In order to mitigate this problem, we propose using the computational power of Graphics Processing Units (GPU) to speedup the computation of image re-ranking algorithms. GPUs are fast emerging and relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. In this paper, we propose a parallel implementation of an image re-ranking algorithm designed to fit the computational model of GPUs. Experimental results demonstrate that relevant performance gains can be obtained by our approach.

This paper presents a novel rank aggregation approach based on contextual information aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) tasks. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information and then re-rank collection images. We conducted several experiments involving shape, color, and texture descriptors. We also evaluated our method in comparison to other rank aggregation approaches. Experimental results demonstrate the effectiveness of our method.

The objective of Content-based Image Retrieval (CBIR) systems is to return the most similar images given an image query. In this scenario, accurately ranking collection images is of great relevance. In general, CBIR systems consider only pairwise image analysis, that is, compute similarity measures considering only pair of images, ignoring the rich information encoded in the relations among several images. This paper presents a novel re-ranking approach based on contextual spaces aiming to improve the effectiveness of CBIR tasks, by exploring relations among images. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information. The re-ranking method can also be applied to other tasks, such as: (i) for combining ranked lists obtained by using different image descriptors (rank aggregation); and (ii) for combining post-processing methods. We conducted several experiments involving shape, color, and texture descriptors and comparisons to other post-processing methods. Experimental results demonstrate the effectiveness of our method.