Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images
Articolo
Data di Pubblicazione:
2008
Citazione:
Fuzzy Clustering With Partial Supervision in
Organization and Classification of Digital Images / W. Pedrycz, A. Amato, V. Di Lecce, V. Piuri. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - 16:4(2008 Aug 11), pp. 1008-1026.
Abstract:
In a Web-oriented society, organization, retrieval, and classification of digital images have become one of the major endeavors.
In this paper, we study themechanisms of fuzzy clustering and fuzzy clustering with partial supervision in the analysis and classification of images. It is demonstrated that themain features of fuzzy clustering become essential in revealing the structure in a collection of images and supporting their classification. The discussed
operational framework of fuzzy clustering is realized by means of fuzzy c-means (FCM). When dealing with the mode of partial supervision,
we augment an original objective function guiding the clustering process by an additional component expressing a level of coincidence between the membership degrees produced by the FCM and class allocation supplied by the user(s). The study also
contrasts the use of the technology of fuzzy sets in image clustering with other approaches studied in this area. A suite of experiments deals with two collections of images, namely, Columbia object image library (COIL-20) and a database composed of 2000 outdoor images.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
angular spectrum signature ; fuzzy c-means
(FCM) ; fuzzy clustering ; human-centric systems ; image classification ; partial supervision ; relevance feedback ; separability index ; fuzzy set theory ; image retrieval ; pattern clustering
Elenco autori:
W. Pedrycz, A. Amato, V. Di Lecce, V. Piuri
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