Effective image retrieval based on hidden concept discovery in image database
This paper addresses content-based image retrieval in general, and in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into regions associated with homo...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 16(2007), 2 vom: 21. Feb., Seite 562-72 |
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| Format: | Aufsatz |
| Sprache: | English |
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2007
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | This paper addresses content-based image retrieval in general, and in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation, a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which the expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10000 general-purposed images demonstrate its promise and effectiveness |
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| Beschreibung: | Date Completed 28.02.2007 Date Revised 26.10.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1941-0042 |