The emergence of large-scale digital image collections in industrial Digital Asset Management (DAM) systems presents significant challenges, requiring classification frameworks capable of managing hierarchical taxonomies and multi-label categorization tasks. This paper presents a comprehensive framework for multi-label hierarchical image classification that bridges the gap between academic research and industrial applications. We propose: (1) a modular architecture with generic hierarchical heads adaptable to any visual backbone, featuring a conditional concatenation strategy for taxonomic consistency; (2) a specialized training approach combining selective backbone fine-tuning with a composite loss function that simultaneously optimizes three metrics. (3) extensive validation on both academic benchmarks and real-world industrial datasets. Experimental results demonstrate competitive performance on academic datasets while achieving promising results on industrial multi-hierarchy scenarios.

Assessing risk factors for intracranial aneurysm (IA) development on images is crucial for early detection of high-risk cases. IAs often form at bifurcations within the circle of Willis (CoW), but manual assessment of these arteries is both time-consuming and susceptible to inconsistencies. Previous studies on imaging markers for IA development lack sufficient evidence for clinical implications, highlighting the need for automated methods to assess CoW morphology. No systematic approach currently exists to identify the best methodological strategies. To address this, we organized a scientific challenge to compare various techniques against a clinical reference standard. Participants were tasked with (1) automated classification of CoW anatomical variants and (2) automated prediction of CoW artery diameters and bifurcation angles. We provided 300 TOF-MRA scans for training and another 300 for testing, all manually annotated. Submissions were evaluated using balanced accuracy, mean absolute error, and Pearson correlation coefficient metrics. This paper provides a detailed analysis of the results from six participating teams. The findings show that various methods may be suitable for automated CoW assessment, but that these need further improvement to meet clinical standards. The challenge remains open for future submissions, offering a benchmark for new techniques.

In this paper, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images (HSI). The main goal of a such representation is to improve image content recognition by focusing the processing only on the most relevant spectral channels. The underlying hypothesis is that for a given category, each image content is better extracted through a specific set of spectral sensitivity functions. In this study, we fixed the number of spectral sensitivity functions to 3 for displaying purposes. Deep features are then extracted from the obtained trichromatic representation of HSI data to build a discriminative image signature. Finally, spectral sensitivity functions are compared in a Content-Based Image Retrieval (CBIR) paradigm. Exhaustive experiments have been conducted on a hyperspectral dataset. Obtained results show the usefulness of the whole spectrum to obtain a discriminative image representation compared to the RGB representation.

Content Based Image Retrieval (CBIR) systems retrieve the most similar images to a query image in a collection. One of the most popular models and widely applied in this task is the Bag of Visual Words model (BoVW). In this paper, we introduce an evaluation study of different information gain models used for the construction of a visual word vocabulary. In the proposed framework, the information gain is used as discriminative information to index image features and select the ones that have the highest values of information gain. The empirical experiments made for this study evaluate the effect of four different information gain models: tf-idf, entropy, bm25, tfc with respect to different descriptors and image databases. The results show that selecting the image features based on at least one of the studied information gain model allows the retrieval process to be more accurate than the classical Bag of Visual Words model.

In content based image retrieval, one of the most important step is the construction of image signatures. To do so, a part of state-of-the-art approaches propose to build a visual vocabulary. In this paper, we propose a new methodology for visual vocabulary construction that obtains high retrieval results. Moreover, it is computationally inexpensive to build and needs no prior knowledge on features or dataset used.

Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. We propose a higher-level visual representation that enhances the traditional part-based Bag of Visual Words (BOW) representation in two aspects. Firstly, we introduce a new multilayer semantic significance analysis (MSSA) model to select Semantically Significant Visual Words (SSVWs) from the classical visual words in order to overcome the noisiness of the feature quantization process. Secondly, we strengthen the discrimination power of SSVWs by constructing Semantically Significant Visual Phrases (SSVPs) from frequently co-occurring SSVWs in the same local context that are semantically coherent. Finally, the large-scale extensive experimental results show that the proposed higher-level visual representation outperforms the traditional part-based image representation in social image retrieval.

In this paper, we present a new approach to count the number of people that cross a counting line from monocular video images. The proposed approach accumulates image slices and estimates the optical flow on them. Then, it performs an online blob detection on these slices in order to extract the crossing persons. The number of persons associated to each blob is determined using a linear regression model applied to blob features which are the position, velocity, orientation and size. The proposed approach is validated on several datasets captured using either a vertical overhead or an oblique mounted camera. The real-time performance and the high counting accuracy of this approach in indoor and outdoor environments are also demonstrated.

Having effective methods to access the desired images is essential nowadays with the availability of huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists in weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

In this paper, we present a user study on a novel video search interface based on the concept of aspect browsing. We aim to confirm whether automatically suggesting new aspects can increase the performance of an aspect-based browser. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. We use a clustering technique to identify potential aspects and use the results to propose suggestions to the user to help them in their search task. We evaluate this approach by analysing the users' perception and by exploiting the log files.

Eye movements are certainly the most natural and repetitive movement of a human being. The most mundane activity, such as watching television or reading a newspaper, involves this automatic activity which consists of shifting our gaze from one point to another. Identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification technique that is based on clustering of eye positions, using projections and projection aggregation applied to static pictures. We also present a new method that computes dispersion of eye fixations in videos considering a multiuser environment. To demonstrate the performance and usefulness of our approach we discuss our experimental work with two different applications: on fixed image and video.

Motivated by the needs for efficient indexing structures adapted to real applications in video database, we present a new indexing structure named Kpyr. In Kpyr, we use a clustering algorithm to partition the data space into sub-spaces on which we apply Pyramid technique (S. Berchtold, et al., 1998). We thus reduce the search space concerned by a query and improve the performances. We show that our approach provides interesting and performing experimental results for both K-nearest neighbors and window queries