IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Contourlet-based texture classification with product bernoulli distributions
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Identifying potentially cancerous tissues in chromoendoscopy images
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Texture classification based on contourlet subband clustering
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Expert Systems with Applications: An International Journal
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Ellipse Invariant Algorithm for Texture Classification
Fundamenta Informaticae
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Statistical texture retrieval in noise using complex wavelets
Image Communication
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This paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition. The probability density function of the singular values of wavelet transformation coefficients of image textures is modeled as an exponential function. The model parameter of the exponential function is estimated using maximum likelihood estimation technique. Truncation of lower singular values is employed to classify textures in the presence of noise. Kullback-Leibler distance (KLD) between estimated model parameters of image textures is used as a similarity metric to perform the classification using minimum distance classifier. The exponential function permits us to have closed-form expressions for the estimate of the model parameter and computation of the KLD. These closed-form expressions reduce the computational complexity of the proposed approach. Experimental results are presented to demonstrate the effectiveness of this approach on the entire 111 textures from Brodatz database. The experimental results demonstrate that the proposed approach improves recognition rates using a lower number of parameters on large databases. The proposed approach achieves higher recognition rates compared to the traditional subband energy-based approach, the hybrid IMM/SVM approach, and the GGD-based approach.