Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using Curvelet Statistical and Co-occurrence Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Inspection of surface defects in copper strip using multivariate statistical approach and SVM
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
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In the field of Content-Based Image Retrieval (CBIR), the semantic understanding of textures has long been a difficult problem, especially the texture classification. This paper proposes a new approach for texture classification, which adopts ten words describing textures in natural language. Texture features of an image are extracted by Discrete Wavelet Transform (DWT), and then classified through both Back Propagating Neural Network (BPNN) and Support Vector Machine (SVM) classifiers. Experimental results show that this approach of texture classification for natural texture is feasible.