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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ben Ismail, M.M. Frigui, H. |
| Copyright Year | 2010 |
| Description | Author affiliation: Multimedia Research laboratory, CECS dept. University of Louisville, USA (Ben Ismail, M.M.; Frigui, H.) |
| Abstract | We propose a novel image database categorization approach using a possibilistic clustering algorithm. The proposed algorithm is based on a robust data modeling using the Generalized Dirichlet (GD) finite mixture and generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership represents the degree of “typicality” and is used to indentify and discard noise points. The algorithm minimizes one objective function to optimize GD mixture parameters and possibilistic membership values. This optimization is done iteratively by dynamically updating the density mixture parameters and the membership values in each iteration. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. The results are compared with those obtained by the Fuzzy C-means algorithm. |
| Starting Page | 334 |
| Ending Page | 339 |
| File Size | 1001555 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424472475 |
| ISSN | 2154512X |
| e-ISBN | 9781424472499 |
| DOI | 10.1109/IPTA.2010.5586778 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-07 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image databases Image color analysis Image edge detection Noise Clustering algorithms Data models Image database categorization clustering Partitioning algorithms mixture models density estimation |
| Content Type | Text |
| Resource Type | Article |
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