The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation based on situational DCT descriptors
Pattern Recognition Letters
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Performance of similarity measures based on histograms of local image feature vectors
Pattern Recognition Letters
An effective method for color image retrieval based on texture
Computer Standards & Interfaces
DCT-Domain image retrieval via block-edge-patterns
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Noise tolerant local binary pattern operator for efficient texture analysis
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
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Information extraction from images and video has been traditionally done in the pixel domain. Currently great majority of pictures and video are available in compressed form with compression based on block DCT transform. Compression removes significant amount of information leaving only perceptually important part and this has potential advantage from the information retrieval point. Optimization of compression for retrieval purposes is thus of interest but topic has not been much emphasized in the past. In this paper we study the problem of image database retrieval from the compression perspective. The approach is based on histograms of quantized DCT blocks. We show how these histograms can be optimized in order to achieve best retrieval performance by optimizing the selection of quantization factor and the number of DCT blocks under normalization of luminance. Results of experiments on face databases show that optimized histograms are robust in retrieval tasks. This indicates that selection and local feature compression optimization is an important step for effective pattern retrieval.