caa a22 4500 605447268 CHVBK 20210128100131.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s11042-013-1820-2 doi (NATIONALLICENCE)springer-10.1007/s11042-013-1820-2 A fast and efficient fuzzy approximation-based indexing for CBIR [Elektronische Daten] [Imane Daoudi, Khalid Idrissi] Crisp index structures introduce the problem of having sharp decision boundaries which may not be found in the real life clustering problems. In real world, specifically in the CBIR context, each data may not be fully assigned to one cluster and it may partially belong to other clusters, as opposed to the crisp index structures which fully affect data to clusters according to their proximity in terms of distance in the high-dimensional vector space. Based on kernel-fuzzy C-means clustering (KFCM) mechanism, this paper presents a fast and efficient index structure to support high-dimensional indexing for both crisp and fuzzy data. The proposed index structure offers a number of advantages such as a compact and efficient fuzzy data clustering. The experimental study demonstrates the efficiency and effectiveness of our method. Springer Science+Business Media New York, 2014 High-dimensional vector space nationallicence CBIR nationallicence Fuzzy clustering nationallicence Approximation-based indexing nationallicence K-NN search nationallicence Daoudi Imane Université Hassan II Ain Chock, ENSEM de Casablanca, LISER/GREENTIC, BP 8118 Oasis Maroc, Casablanca, Morroco aut Idrissi Khalid Université de Lyon, CNRS, INSA de Lyon, LIRIS, UMR5205, 69621, Lyon, France aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4507-4533 1380-7501 74:13<4507 2015 74 11042 https://doi.org/10.1007/s11042-013-1820-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1820-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Daoudi Imane Université Hassan II Ain Chock, ENSEM de Casablanca, LISER/GREENTIC, BP 8118 Oasis Maroc, Casablanca, Morroco aut NATIONALLICENCE 700 1- Idrissi Khalid Université de Lyon, CNRS, INSA de Lyon, LIRIS, UMR5205, 69621, Lyon, France aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4507-4533 1380-7501 74:13<4507 2015 74 11042