On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Characterization and detection of noise in clustering
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Categorization of Image Databases for Efficient Retrieval Using Robust Mixture Decomposition
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
On Competitive Unsupervised Clustering
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Spatial Color Indexing and Applications
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Clustering by competitive agglomeration
Pattern Recognition
IEEE Transactions on Multimedia
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We introduce a new robust approach to categorize image databases: Adaptative Robust Competition (ARC). Providing the best overview of an image database helps users browsing large image collections. Estimating the distribution of image categories and finding their most descriptive prototype represent the two main issues of image database categorization. Each image is represented by a high-dimensional signature in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Image database overview by categorization is computed in challenging conditions since clusters are overlapping and the number of clusters is unknown. Clustering is performed by minimizing a Competitive Agglomeration objective function with an extra noise cluster collecting outliers.