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 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.
fuzzy clustering partial supervision fuzzy c-means (fcm) angular spectrum signature human-centric systems image classification relevance feedback separability index