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The 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 some problems (consistency, robustness, many input parameters). In this article we present a new eye fixation identification technique that is based on clustering of eye positions using projections and projection aggregation.

With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI)produced by different types of imaging sensors, analyzing and retrieving these images requireeffective image description and quantification techniques. Compared to remote sensing RGB images,HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowingprofile materials and organisms that only hyperspectral sensors can provide. In this article, we studythe importance of spectral sensitivity functions in constructing discriminative representation ofhyperspectral images. The main goal of such representation is to improve image content recognitionby focusing the processing on only the most relevant spectral channels. The underlying hypothesisis that for a given category, the content of each image is better extracted through a specific set ofspectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-BasedImage Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remotesensing community, specifically designed for Hyperspectral remote sensing retrieval and classification.Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtainedretrieval results prove that the physical measurements and optical properties of the scene containedin the HSI contribute in an accurate image content description than the information provided by theRGB image presentation.

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In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness.

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International audienceIn 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.Classically, the vocabulary is built by aggregating a certain number of features in centroids using a clustering algorithm. The final centroids are assimilated to visual "words". Our approach for building a visual vocabulary is based on an iterative random visual word selection mixing a saliency map and tf-idf scheme. Experiment results show that it outperforms the original "Bag of visual words" based approach in efficiency and effectiveness