Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round
In this paper, we address the selection in the context of Content Based-Image Retrieval (CBIR). Instead of addressing features’ selection issue, we deal here with distance selection as a novel paradigm poorly addressed within CBIR field. Whereas distance concept is a very precise and sharp mathemati...
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Veröffentlicht in: | Egyptian informatics journal 2017-03, Vol.18 (1), p.1-9 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In this paper, we address the selection in the context of Content Based-Image Retrieval (CBIR). Instead of addressing features’ selection issue, we deal here with distance selection as a novel paradigm poorly addressed within CBIR field. Whereas distance concept is a very precise and sharp mathematical tool, we extend the study to weak distances: Similarity, quasi-distance, and divergence. Therefore, as many as eighteen (18) such measures as considered: distances: {Euclidian, …}, similarities{Ruzika, …}, quasi-distances: {Neyman-X2, …} and divergences: {Jeffrey, …}. We specifically propose a hybrid system based on the Sequential Forward Selector (SFS) meta-heuristic with one round and relevance feedback. The experiments conducted on the Wang database (Corel-1K) using color moments as a signature show that our system yields promising results in terms of effectiveness. |
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ISSN: | 1110-8665 2090-4754 |
DOI: | 10.1016/j.eij.2016.09.001 |