Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Effects of Different Gabor Filter Parameters on Image Retrieval by Texture
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Multimedia analysis for ecological data
Proceedings of the 20th ACM international conference on Multimedia
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This paper describes a visual processing technique for automatic frog (Xenopus Laevis sp.) localization and identification. The problem of frog identification is to process and classify an unknown frog image to determine the identity which is recorded previously on an image database. The frog skin pattern (i.e. texture) provides a unique feature for identification. Hence, the study investigates three different kind of features (i.e. Gabor filters, granulometry, threshold set compactness) to extract texture information. The classifier is built on nearest neighbor principle; it assigns the query feature to the database feature which has the minimum distance. Hence, the study investigates different distance measures and compares their performance. The detailed results show that the most successful feature and distance measure is granulometry and weighted L1 norm for the frog identification using skin texture features.