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Compared to traditional 2D image techniques, stereoscopic techniques provide additional information-the binocular depth, which strongly enhances the immersion. However, it may also cause visual comfort problems because it is still not a perfect representation of natural human vision but to some extent an illusion. How to fairly evaluate and understand the(More)
In this work, we present an extended study of image representations for fine-grained classification with respect to image resolution. Understudied in literature, this parameter yet presents many practical and theoretical interests, e.g. in embedded systems where restricted computational resources prevent treating high-resolution images. It is thus(More)
In this paper, the new challenges of 3DTV for subjective assessment are discussed. Conventional 2D methods have severe limitations which will be revealed. Based on the understanding of the new characteristics brought by 3DTV, changes and additions in the requirements for subjective assessment are proposed in order to develop a novel subjective video quality(More)
This paper presents RETIN, a new system for automatic image indexing and interactive content-based image retrieval. The most original aspect of our work rests on the distance computation and its adjustment by relevance feedback. First, during an offline stage, the indexes are computed from attribute vectors associated with image pixels. The feature spaces(More)
This paper presents a new learning technique for the similarity model refinement in CBIR systems. We propose a whole retrieval strategy based on a new relevance feedback scheme and on a long-term similarity learning algorithm which uses feedback information of previous sessions. We introduce this technique as the simple evolution of the short-term relevance(More)
Modern stereoscopic 3DTV brings new QoE (quality of experience) to viewers, which not only enhances the 3D sensation due to the added binocular depth, but may also induce new problems such as visual discomfort. Subjective quality assessment is the conventional method to assess the QoE. However, the conventional perceived image quality concept is not enough(More)
Content-based image retrieval (CBIR) usually relies on pre-attentive similarities. Results are often coarse because of the gap between the pre-attentive level and the semantic level of the user's request. The aim of relevance feedback is to refine results by taking user's expertise into account. This paper presents a new feedback architecture for CBIR.(More)
In this paper, we present a new version of our content-based image retrieval system RETIN. It is based on adaptive quantization of the color space, together with new features aiming at representing the spatial relationship between colors. Color analysis is also extended to texture. Using these powerful indexes, an original interactive retrieval strategy is(More)
This paper introduces a flexible search strategy for image or image category retrieval in large databases. The process is based on a search-by-similarity method in which both request and dissimilarity measure are updated thanks to user interaction. The system copes with complex queries combining relevant images scattered in the database. The effectiveness(More)