Frigui, Hichem, Nozha Boujemaa and Soon-Ann Lim, "Unsupervised Clustering and Feature Discrimination with Application to Image Database Categorization," in Proceedings of the IFSA World Congress and 20th NAFIPS International Conference, 2001, pp. 401-406. Home/Search Document Details and Download
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Image Retrieval on the Internet - How can Fuzzy Help? - Walker (2002) (Correct)
....Clustering is important in several factors of information retrieval. In traditional information retrieval, one important means of speedup is to cluster data and to represent only a representative of each cluster in the database. 11] Already, clustering has been used in organizing image databases [9]. When the source of information is the Internet, clustering the results allows more useful information to be presented on the first page of the results, allowing the user to determine which cluster is relevant. Clustering was introduced to the web as a method of limiting the number of documents ....
Frigui, Hichem, Nozha Boujemaa and Soon-Ann Lim, "Unsupervised Clustering and Feature Discrimination with Application to Image Database Categorization," in Proceedings of the IFSA World Congress and 20th NAFIPS International Conference, 2001, pp. 401-406.
Unsupervised Robust Clustering for Image Database Categorization - Le Saux, Boujemaa (2002) (1 citation) Self-citation (Boujemaa) (Correct)
....algorithms. The fuzzy version (Fuzzy C Means [1] has been constantly improved for twenty years, by the use of the Mahalanobis distance [6] the adjunction of a noise cluster [3] or the competitive agglomeration algorithm [5] 2] Specific algorithms have been developed for the categorization [8] [4] and the browsing [11] of image databases. This paper is organized as follows. 2 presents the background of our work. Our method is presented in 3. The results on image databases are discussed and compared with other clustering methods in 4, 5 summarizes our concluding remarks. 2. Background The ....
....of features The features have different orders of magnitude and different dimensions, so the distance cannot be a simple sum of partial distances. The idea is to learn the weights in equation (14) during the clustering process. Ordered Weight Averaging [12] is used, as proposed in [4]. First, partial distances are sorted in ascending order. For each feature , the average rank of corresponding partial distance over images is obtained : 3 10 4 K= 4 4 =7 7 7 4 S (17) And the weight at iteration is updated using : d 0 S x K 7 ....
[Article contains additional citation context not shown here]
H. Frigui, N. Boujemaa, and S.-A. Lim. Unsupervised clustering and feature discrimination with application to image database categorization. In NAFIPS, Vancouver, Canada, 2001.
Unsupervised Categorization for Image Database Overview - Le Saux, Boujemaa Self-citation (Boujemaa) (Correct)
.... for twenty years by the use of the Mahalanobis distance [2] the adjunction of a noise cluster [3] or the competitive agglomeration algorithm [4] 5] A few attempts to organize and browse image databases have been made: Brunelli and Mich [6] Medasani and Krishnapuram [7] and Frigui et al. [8]. A key point of categorization is the input data representation. A set of signatures (color, texture and shape) allows to describe the visual appearance of the image. The content based categorization should be performed by clustering these signatures. This operation is computed in challenging ....
....The features have different orders of magnitude and different dimensions, so the distance over all features cannot be defined as a simple sum of partial distances for each feature. The idea is to learn the weights during the clustering process. Ordered Weight Averaging [12] is used, as proposed in [8]. First, partial distances are sorted in ascending order. For each feature , the rank of corresponding partial distance is obtained: K fKX 3c 4 ; 4 = 7=7 (18) And the weight at iteration is updated using: S 4 K 7 4 X7 (19) It has two positive effects. ....
[Article contains additional citation context not shown here]
Frigui, H., Boujemaa, N., Lim, S.A.: Unsupervised clustering and feature discrimination with application to image database categorization. In: NAFIPS, Vancouver, Canada (2001)
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