Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.

In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare both methods (WLC-GA and its extension) with a more complex non-linear meta learning strategy called Stacked Generalization in which Support Vector Machines with Radial Basis Function kernel was used as combiner [2]. The experimental results, considering two widely used data sets, the Indian Pines and the Pavia University, are conducted in three different scenarios. Results show that both WLC-GA and its extended version achieve the best overall accuracy, and the proposed classification approach overcomes the accuracies of the other traditional ones used in this study.

Remote sensed hyperspectral images have been used for many purposes and have become one of the most important tools in remote sensing. Due to the large amount of available bands, e.g., a few hundreds, the feature extraction step plays an important role for hyperspectral images interpretation. In this paper, we extend a well-know feature extraction method called Extended Morphological Profile (EMP) which encodes spatial and spectral information by using Partial Least Squares (PLS) to emphasize the importance of the more discriminative features. PLS is employed twice in our proposal, i.e., to the EMP features and to the raw spatial information, which are then concatenated to be further interpreted by the SVM classifier. Our experiments in two well-known data sets, the Indian Pines and Pavia University, have shown that our proposal outperforms the accuracy of classification methods employing EMP and other baseline feature extraction methods with different classifiers.

Plant phenology has been exploited as an important research venue for assessing the impact of climate changes. One common approach for monitoring vegetation relies on the use of digital cameras. The employment of imaging techniques for phenological observation allows the extraction and analysis of visual characteristics based on color and texture information with the objective of determining plant life cycle changes, such as the beginning of the leaf flushing or the senescence period. This paper presents a novel approach for detecting phenological changes by analyzing image temporal series. Our method is based on the use of visual rhythm analysis and the adoption of a dissimilarity measure to detect visual changes in the time line. Experiments were conducted on a three-year data set composed of 3,538 vegetation images and 21 samples of 6 different species of interest. Results demonstrate that the proposed change detection approach is able to effectively identify phenological events.

In this work, we investigate how much content-based visual information analysis can aid in filtering spam videos on video sharing social networks. That is a very challenging task, not only because of the high-level semantic concepts involved, but also because the diverse nature of social net-works prevents the use of constrained a priori information. In addition, a spam video is, by nature, context-dependent. We propose a context-aware description, which improves detection considerably in comparison with the baseline bags-of-visual-words model, by allowing us to incorporate the context of the video into the representation. Our model is evaluated in two challenging video dataset, showing very encouraging results.

In the past few years, hyperspectral images have been considered as one of the most important tool in land cover classification due to its capability to obtain rich information of materials on earth surface. In this work we aim to produce an accurate thematic map for the remote sensed hyperspectral image classification problem, which is obtained using a combination of several classification methods. Three types of feature representation and two learning algorithms (Support Vector Machines (SVM) and Backpropagation Multilayer Perceptron Neural Network (MLP)) were used yielding six classification methods to perform the combination. Our combination proposal is based on Weighted Linear Combination (WLC), in which weights are found using a Genetic Algorithm (GA) - WLC-GA. Experiments were carried out with two well-known datasets: Indian Pines and Pavia University, and we observed that our proposed WLC-GA method achieves the highest accuracy among traditional Conscious Combiners, the widely used Majority Vote (MV) and Weighted Majority Vote (WMV), for both datasets.

In image classification, the most powerful statistical learning approaches are based on the Bag-of-Words paradigm. In this article, we propose an extension of this formalism. Considering the Bag-of-Features, dictionary coding and pooling steps, we propose to focus on the pooling step. Instead of using the classical sum or max pooling strategies, we introduced a density function-based pooling strategy. This flexible formalism allows us to better represent the links between dictionary codewords and local descriptors in the resulting image signature. We evaluate our approach in two very challenging tasks of video and image classification, involving very high level semantic categories with large and nuanced visual diversity.

In this paper, we propose a new approach for color image simplification in order to improve flame pixel classification. The fire detection performance depends critically on the performance of the flame pixel classifier. Color image simplification is the process of simplifying an image in order to decrease the number of colors while preserving, as much as possible, shapes. In this work, a hierarchical clustering method in a given color space is used to map the original colors into a smaller set of representative ones, allowing the use of a simple heuristic rule for classifying the clusters related to candidate flame colors. Using reverse mapping, we identify possible flame colors in the image. Main contributions of our work are the application of a simple hierarchical clustering method to color simplification, that decreases the number of possible flame colors, and a filtering methodology to reduce the influence of outliers. Several color spaces and distance measures were used to evaluate the proposed method. Experimental results demonstrate that color simplification is essential to successfully employ heuristic classification of flame colors.

This paper reports a system developed for video browsing based on multimodal analysis. Our multimodal approach performs audio transcription for shot categorization (sports, weather, politics and economy) combining audio and visual information for theme categorization. Its main features include static and dynamic summaries, segmentation using face detection, classification into Indoor/Outdoor scenes based on Support Vector Machine (SVM) and audio transcription for theme keyword search. Keywords are selected to represent the subjects, followed by a simple text search. We conduct a set of experiments for evaluating the effectiveness of the shot subject categorization using audio transcription information.

Sports video analysis has received special attention from researchers due to its high popularity and general interest on semantic analysis. Hence, soccer videos represent an interesting field for research allowing many types of applications: indexing, summarization, players' behavior recognition and so forth. Many approaches have been applied for field extraction and recognition, arc and goalmouth detection, ball and players tracking, and high level techniques such as team tactics detection and soccer models definition. In this paper, we provide an hierarchy and we classify approaches into this hierarchy based on their analysis level, i.e., low, middle, and high levels. An overview of soccer event identification is presented and we discuss general issues related to it in order to provide relevant information about what has been done on soccer video processing.

This paper proposes a content-based video analysis system called EVEREVIS (Event Recognizer in Video Scenes), implemented as a Web-based system. In this prototype, news broadcast videos are segmented into indoor and outdoor scenes in order to facilitate navigation. Video summarization is performed and then the visual information content is characterized using HueSIFT. Support vector machine classifier is used for scenes classification and results show the plausibility of the system.

In this paper, we present a content-based image retrieval (CBIR) system called MammoSVD. This CBIR system is developed based on breast density — fatty or dense, and the database used, from the IRMA project, provides images with the ground truth already set. Singular value decomposition (SVD) is proposed for the breast density characterization by the selection of the first singular values, in order to represent texture along with the dimensionality reduction. Support-vector machine (SVM) is used to perform the retrieval operation. Considering the first 10% of the retrieved images, the precision rate is 90%, indicating the potential of the implemented CBIR system.

In this paper we propose a technique of limiarization (also known as thresholding or binarization) tailored to improve the readability of degraded historical documents. Limiarization is a simple image processing technique, which is employed in many complex tasks like image compression, object segmentation and character recognition. The technique also finds applications on itself: since it results in a high-contrast image, in which the foreground is clearly separated from the background, it can greatly improve the readability of a document, provided that other attributes (like character shape) do not suffer. Our technique exploits statistical characteristics of textual documents and applies both global and local thresholding. Under visual inspection on experiments made in a collection of severely degraded historical documents, it compares favorably with the state of the art.

Video data is becoming increasingly important in many commercial and scientific areas with the advent of applications such as digital broadcasting, video-conferencing and multimedia processing tools, and with the development of the hardware and communications infrastructure necessary to support visual applications. The objective of this work is to propose a method for event detection in a video stream. We combine Harris-SIFT descriptor with motion information in order to detect human actions in video. We tested our method in KTH database and compared it to space-time interest points (STIP) descriptor. The results obtained achieved similar results to the STIP method.

Bag-of-Visual-Features (BoVF) representations have achieved a great success when used for object recognition, mainly because of their robustness to several kinds of variations and occlusion. Recently, a number of BoVF approaches has been proposed also for recognition of human actions from videos. One important issue that arises when using BoVF for videos is how to take dynamic information into account, and most proposals rely on 3D extensions of 2D visual descriptors for this. However, we envision alternative approaches based on 2D descriptors applied to the spatio-temporal video planes, instead of to the traditionally explored by previous work. Thus, in this paper, we address the following question: what is the cost-effectiveness of a BoVF approach built from such 2D descriptors when compared to one based on the state-of-the-art 3D Spatio-Temporal Interest Points (STIPs) descriptor? We evaluate the recognition rate and time complexity of alternative 2D descriptors applied to different sets of spatio-temporal planes, and the state-of-the-art STIPs. Experimental results show that, with proper settings, 2D descriptors can yield the same recognition results as those provided by STIP, but at a significantly higher time complexity.

Abstract We propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. It acts as a three‐dimensional structuring element of smooth surfaces. Robust orientation inference for all input elements is performed by morphological operations using the tensor field. A general normal estimator is defined by combining the inferred normals, their confidences and the tensor field. This estimator can be used to directly reconstruct the surface or give input normals to other reconstruction methods. We present qualitative and quantitative results to show the behavior of the original methods and ours. A comparative discussion of these results shows the efficiency of our propositions.

The video segmentation problem can be regarded as a problem of detecting the fundamental video units (shots). Due to different ways of linking two consecutive shots this task turns out to be difficult. In this work, we propose a method to detect both cuts and gradual transitions by image segmentation tools instead of using dissimilarity measures or mathematical models. Firstly, the video is transformed into a 2D image, called visual rhythm by sub-sampling. Afterwards, we apply image processing tools to detect all vertical aligned transitions in this image. The main operator applied here is the morphological multiscale gradient. We also present some experimental results.

Neutron radiography has been widely employed to investigate corrosion in aluminum aircraft components. A neutron radiographic image analyzer has previously been developed that has embedded several image processing routines (preprocessing, filtering, edge enhancement, segmentation, measurement and, pseudo coloring and image comparison). Radiographic negatives are digitized in two ways: (a) using a ScanJet IIp flat scanner and, (b) using a Sony DXC-107 CCD camera and a monochrome frame-grabber. The good results of the employment of digital image processing on neutron radiographic images for aluminum corrosion detection have encouraged the authors to continue the research and to develop a real-time neutron radiography imaging system.>