Texture discrimination by Gabor functions
Biological Cybernetics
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using joint histograms
Multimedia Systems - Special issue on video content based retrieval
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Combining similarity measures in content-based image retrieval
Pattern Recognition Letters
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CBIR (Content Based Image Retrieval) systems are usually based on a series of distance functions, whose results are combined to produce similarity values which attempt to emulate the user's judgements. Most of these distance functions produce dissimilarity values according to a given vector of features. Although the values they produce are related to the perceptual similarity between pictures, they usually do not have an easily describable meaning. In this paper, we propose a technique to normalize distance functions so that they express probability values. This normalization makes it possible to define a consistent method to combine several distance functions and produce a composite similarity measure which performs better than the original functions. To test the approach, a composite similarity measure has been built from a set of three statistically independent distance functions. Measuring the precision on the first 8 retrieved images for a 100 queries, the composite measure has outperformed the individual distance functions by 29%, and the normalized sum by about 14%.