Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Error analysis of update methods for the symmetric eigenvalue problem
SIAM Journal on Matrix Analysis and Applications
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Matrix computations (3rd ed.)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
Pattern Recognition Methods in Image and Video Databases: Past, Present and Future
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Bimodal System for Interactive Indexing and Retrieval of Pathology Images
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Advanced algorithmic approaches to medical image segmentation
Similarity searching in image retrieval with statistical distance measures and supervised learning
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Distributional distances in color image retrieval with GMVQ-Generated histograms
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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Whenever a feature extracted from an image has a unimodal distribution, information about its covariance matrix can be exploited for content-based retrieval using as dissimilarity measure the Bhattacharyya distance. To reduce the amount of computations and the size of logical database entry, we approximate the Bhattacharyya distance taking into account that most of the energy in the feature space is often restricted to a low dimensional subspace. The theory was tested for a database of 1188 textures derived from VisTex with the local texture being represented by a 15-dimensional MRSAR feature vector. The retrieval performance improved significantly relative to the traditional, Mahalanobis distance based approach in spite of using only one or two dimensions in the approximation.