An efficient retrieval strategy for wavelet-based quantized images

Abstract : Recent research efforts have been devoted to the improvement of image retrieval systems when datasets are represented in a compressed form. In this context, new studies have shown that compres- sion has a negative impact on the performances of the traditional retrieval systems. In this work, we are mainly interested in designing an efficient retrieval approach well adapted to wavelet-based compressed images. More precisely, we first propose to apply a compression scheme based on the Moment Preserving Quantization (MPQ). Then, the feature vectors will be constructed in an appropriate way by focusing on the quantized subbands where some given statistical moments have been preserved. Experimental results indicate that the proposed approach outperforms the most recent one which involves the conventional uniform quantizer and constrains the query and the model images to have similar qualities during the retrieval step.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2013, Canada. 2013
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https://hal.archives-ouvertes.fr/hal-00827590
Contributeur : Mounir Kaaniche <>
Soumis le : mercredi 29 mai 2013 - 14:00:45
Dernière modification le : mercredi 29 mai 2013 - 17:59:03

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  • HAL Id : hal-00827590, version 1

Citation

Amani Chaker, Mounir Kaaniche, Amel Benazza-Benyahia. An efficient retrieval strategy for wavelet-based quantized images. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2013, Canada. 2013. <hal-00827590>

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