Content adaptation resilient low-level visual features for content based image retrieval

In content based image retrieval (CIBR), low-level visual features are usually extracted from the highest quality and resolutions of visual contents. When content is scalable coded, their bit streams can be adapted at various nodes in multimedia usage chains to cater the variations in network bandwidths, display device resolutions and resources and usage preferences by just discarding insignificant resolution-quality layers. This can result in the existence of different version of the same content with dissimilar low-level visual features. Therefore, mapping of low level visual descriptors into content resolution-quality spaces is important in order to obtain low-level visual features that are resilient to content adaptations. A new scalable domain feature extraction using the compression modes and decisions is presented and its CBIR performance is evaluated. The proposed scheme outperforms MPEG-7 visual descriptors in both the original image and scaled resolution-quality space domains.