Mohammed Lamine Kherfi

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With the explosive growth of the World Wide Web, the public is gaining access to massive amounts of information. However, locating needed and relevant information remains a difficult task, whether the information is textual or visual. Text search engines have existed for some years now and have achieved a certain degree of success. However, despite the(More)
In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the(More)
In this paper, we address some issues related to the combination of positive and negative examples to perform more efficient image retrieval. We analyze the relevance of negative example and how it can be interpreted. Then we propose a new relevance feedback model that integrates both positive and negative examples. First, a query is formulated using(More)
We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more(More)
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