The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Memory-Based Face Recognition for Visitor Identification
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Jensen-Shannon Boosting Learning for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Recognition Using Landmark-Based Bidimensional Regression
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Face Recognition Using IPCA-ICA Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
DCT histogram optimization for image database retrieval
Pattern Recognition Letters
How effective are landmarks and their geometry for face recognition?
Computer Vision and Image Understanding
Pattern Recognition
Tree-structured image difference for fast histogram and distance between histograms computation
Pattern Recognition Letters
Nested Partitions Properties for Spatial Content Image Retrieval
International Journal of Digital Library Systems
Engineering Applications of Artificial Intelligence
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We investigate similarity measures for image retrieval from databases based on histograms of local feature vectors. The feature vectors are obtained from grouping quantized block transforms coefficients and thresholding. After preliminaries on block transforms we are introducing binary DC and AC feature vectors. Subsequently ternary DC and AC vectors are defined. Next we show how the histograms of vectors defined can be combined to form similarity measure for image retrieval from database. We formulate the database training and retrieval problem using the defined similarity measures. Performance results are shown using widely used FERET and ORL databases and the cumulative match score evaluation. We show that despite simplicity the proposed measures provide results which are on par with best results using other methods. This indicates that statistics based retrieval should not be underestimated comparing to structural methods.