Classification of Image Data Using Gradient-Based Fuzzy C-Means with Mercer Kernel
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A data-mining based skin detection method in JPEG compressed domain
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Error concealment method selection in texture images using advanced local binary patterns classifier
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Fuzzy c-means algorithm with divergence-based kernel
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Content-Based classification of images using centroid neural network with divergence measure
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Satellite image classification using a divergence-based fuzzy c-means algorithm
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Video saliency detection in the compressed domain
Proceedings of the 20th ACM international conference on Multimedia
Image data classification using fuzzy c-means algorithm with different distance measures
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The multiresolution wavelet transform has been shown to be an effective technique and achieved very good performance for texture analysis. However, a large number of images are compressed by methods based on the discrete cosine transform (DCT). Hence, the image decompression of the inverse DCT is needed to obtain the texture features based on the wavelet transform for the DCT-coded image. This paper proposes the use of the multiresolution reordered features for texture analysis. The proposed features are directly generated by using the DCT coefficients from the DCT-coded image. Comparisons with the subband-energy features extracted from the wavelet transform, conventional DCT using the Brodatz (1966) texture database indicate that the proposed method provides the best texture pattern retrieval accuracy and obtains a much better correct classification rate. The proposed DCT based features are expected to be very useful and efficient for texture pattern retrieval and classification in large DCT-coded image databases.