An efficient High-Dimensionnal Indexing Method for Content-Based Image Retrieval in Large Image Databases

Abstract : High-dimensional indexing methods have been proved quite useful for response time improvement. Based on Euclidian distance, many of them have been proposed for applications where data vectors are high-dimensional. However, these methods do not generally support efficiently similarity search when dealing with heterogeneous data vectors. In this paper, we propose a high-dimensional indexing method (KRA+-Blocks) as an extension of the region approximation approach to the kernel space. KRA+-Blocks combines nonlinear dimensionality reduction technique (KPCA) with region approximation approach to map data vectors into a reduced feature space. The created feature space is then used, on one hand to approximate regions, and on the other hand to provide an effective kernel distances for both filtering process and similarity measurement. In this way, the proposed approach achieves high performances in response time and in precision when dealing with high dimensional and heterogeneous vectors.
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Article dans une revue
EURASIP Journal, Signal processing : Image communication, 2009, 24, pp.775-790
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https://hal.archives-ouvertes.fr/hal-01437769
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Soumis le : mardi 17 janvier 2017 - 13:58:46
Dernière modification le : jeudi 1 novembre 2018 - 01:19:38

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  • HAL Id : hal-01437769, version 1

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Imane Daoudi, Khalid Idrissi, Said Ouatik, Atilla Baskurt, Driss Aboutajdine. An efficient High-Dimensionnal Indexing Method for Content-Based Image Retrieval in Large Image Databases. EURASIP Journal, Signal processing : Image communication, 2009, 24, pp.775-790. 〈hal-01437769〉

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