Weighted pseudo-metric for a fast CBIR method

• Autorzy: Ksantini R. , Ziou D. , Colin B. , Dubeau F.
• Konferencja: International Conference on Computer Vision and Graphics ICCVG 2006 (25-27.09.2006 ; Warsaw, Poland)
• Języki publikacji: EN

Abstrakty

EN

In this paper, a simple and fast querying method for content-based image retrieval is presented. In order to measure the similarity degree between two color images both quickly and effectively, we use a weighted pseudo-metric employing one-dimensional Daubechies decomposition and compression of the extracted feature vectors. In order to improve the discriminatory capacity of the pseudo-metric, we compute its weights using separately a classical logistic regression model and a Bayesian logistic regression model. The Bayesian logistic regression model was shown to be significantly better than the classical logistic regression model at improving the retrieval performance. Experimental results are reported on the WANG and ZuBuD color image databases proposed by [Deselaers T., Keysers D., Ney H.: Classification error rate for quantitative evaluation of content-based image retrieval systems. 17th International Conference on Pattern Recognition (ICPR'04), 2, pp. 505-508, Cambridge, UK].

Słowa kluczowe

EN

color image retrieval; weighted pseudo-metric; logistic regression models

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2006
• Tom: Vol. 15, No. 3/4
• Strony: 471--480
• Opis fizyczny: Bibliogr. 16 poz., wykr.

Twórcy

autor

Ksantini R.

autor

Ziou D.

autor

Colin B.

autor

Dubeau F.

• Department of Computer Science. Faculty of Computer Sciences, University of Sherbrooke. Sherbrooke, QC, Canada, J1K 2R1

Bibliografia

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