A Semi-Supervised Metric Learning for Content-Based Image Retrieval

Imane Daoudi 1 Khalid Idrissi 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, the authors propose a kernel-based approach to improve the retrieval performances of CBIR systems by learning a distance metric based on class probability distributions. Unlike other metric learning methods which are based on local or global constraints, the proposed method learns for each class a nonlinear kernel which transforms the original feature space to a more effective one. The distances between query and database images are then measured in the new space. Experimental results show that the kernelbased approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.
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Article dans une revue
Journal of Computer Vision and lmage Processing, 2011, 3, 1, pp.53-64. 〈10.4018/ijcvip.2011070104〉
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Soumis le : vendredi 19 août 2016 - 17:47:01
Dernière modification le : jeudi 19 avril 2018 - 14:38:06

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Imane Daoudi, Khalid Idrissi. A Semi-Supervised Metric Learning for Content-Based Image Retrieval. Journal of Computer Vision and lmage Processing, 2011, 3, 1, pp.53-64. 〈10.4018/ijcvip.2011070104〉. 〈hal-01354870〉

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