Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images
In a Web-oriented society, organization, retrieval, and classification of digital images have become one of the major endeavors. In this paper, we study the mechanisms of fuzzy clustering and fuzzy clustering with partial supervision in the analysis and classification of images. It is demonstrated t...
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| Veröffentlicht in: | IEEE transactions on fuzzy systems 2008-08, Vol.16 (4), p.1008-1026 |
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| creator | Pedrycz, W. Amato, A. Di Lecce, V. Piuri, V. |
| description | In a Web-oriented society, organization, retrieval, and classification of digital images have become one of the major endeavors. In this paper, we study the mechanisms of fuzzy clustering and fuzzy clustering with partial supervision in the analysis and classification of images. It is demonstrated that the main 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. |
| doi_str_mv | 10.1109/TFUZZ.2008.917287 |
| format | Article |
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In this paper, we study the mechanisms of fuzzy clustering and fuzzy clustering with partial supervision in the analysis and classification of images. It is demonstrated that the main 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.</description><subject>Angular spectrum signature</subject><subject>Classification</subject><subject>Clustering</subject><subject>Collection</subject><subject>Digital</subject><subject>Digital images</subject><subject>Feedback</subject><subject>Fuzzy</subject><subject>fuzzy c-means (FCM)</subject><subject>fuzzy clustering</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>human-centric systems</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image classification</subject><subject>Image retrieval</subject><subject>partial supervision</subject><subject>relevance feedback</subject><subject>Road vehicles</subject><subject>separability index</subject><subject>Societies</subject><subject>Studies</subject><subject>Supervision</subject><issn>1063-6706</issn><issn>1941-0034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kU9Lw0AQxYMoWKsfQLwED3pKncludjdHqVYLQgVbhB5cNummbkmTupsI7ad3a8SDB0_zh98b3vCC4BxhgAjpzXQ0m88HMYAYpMhjwQ-CHqYUIwBCD30PjESMAzsOTpxbASBNUPSCt1G7223DYdm6RltTLcNX07yHz8o2RpXhS7vR9tM4U1ehqcKJXarK7FSzn1W18DrlnClM3q3qIrwzS9N45XitltqdBkeFKp0--6n9YDa6nw4fo6fJw3h4-xTlJEmaKIsXjCtvT3HMkzTli5hSQmkhMCsyAihS75-ThEBaZAtkNFNM0IyrWCgBivSD6-7uxtYfrXaNXBuX67JUla5bJ4UARoVIwJNX_5KEJhALih68_AOu6tZW_guZYkxAIGUewg7Kbe2c1YXcWLNWdisR5D4Y-R2M3Acju2C85qLTGK31L08ZICInX_cXiRA</recordid><startdate>20080801</startdate><enddate>20080801</enddate><creator>Pedrycz, W.</creator><creator>Amato, A.</creator><creator>Di Lecce, V.</creator><creator>Piuri, V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Angular spectrum signature Classification Clustering Collection Digital Digital images Feedback Fuzzy fuzzy c-means (FCM) fuzzy clustering Fuzzy logic Fuzzy set theory Fuzzy sets Fuzzy systems human-centric systems Humans Image analysis Image classification Image retrieval partial supervision relevance feedback Road vehicles separability index Societies Studies Supervision |
| title | Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images |
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