Multivariate indexing of multichannel images

Sarra Sakji; Amel Benazza-Benyahia

doi:10.1117/12.736391

2 October 2007 Multivariate indexing of multichannel images

Sarra Sakji, Amel Benazza-Benyahia

Proceedings Volume 6763, Wavelet Applications in Industrial Processing V; 67630E (2007) https://doi.org/10.1117/12.736391
Event: Optics East, 2007, Boston, MA, United States

Abstract

In this work, we address the problem of multichannel image retrieval in the compressed domain. A wavelet transform is applied to each component of the multispectral image. The salient features are computed from the resulting wavelet subbands. To this purpose, two approaches are envisaged. In the first one, the wavelet coeffcients of each component are separately considered whereas in the second one, they are jointly processed. More precisely, the contribution of this work lies on the fact that the features are extracted from the multivariate distribution of the wavelet coeffcients modelized thanks to copulas. Experimental results indicate that the second approach gives the best performances in terms of precision and recall.

Citation Download Citation

Sarra Sakji and Amel Benazza-Benyahia "Multivariate indexing of multichannel images", Proc. SPIE 6763, Wavelet Applications in Industrial Processing V, 67630E (2 October 2007); https://doi.org/10.1117/12.736391

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