An efficient high-dimensional indexing method for content-based retrieval in large image databases / I. Daoudi in Signal processing. Image communication, Vol. 24 N° 10 (Novembre 2009)  

Public ISBD [article]  inSignal processing. Image communication > Vol. 24 N° 10 (Novembre 2009) . - pp. 775-790 Titre : An efficient high-dimensional indexing method for content-based retrieval in large image databases Type de document : texte imprimé Auteurs : I. Daoudi, Auteur ; K. Idrissi, Auteur ; S.E. Ouatik, Auteur Article en page(s) : pp. 775-790 Note générale : Electronique Langues : Anglais (eng) Mots-clés : CBIR High-dimensional vector space Region approximation approach Kernel Image databases Relevance feedback Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : 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. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...] [article] An efficient high-dimensional indexing method for content-based retrieval in large image databases [texte imprimé] / I. Daoudi, Auteur ; K. Idrissi, Auteur ; S.E. Ouatik, Auteur . - pp. 775-790. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 24 N° 10 (Novembre 2009) . - pp. 775-790 Mots-clés : CBIR High-dimensional vector space Region approximation approach Kernel Image databases Relevance feedback Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : 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. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...]

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