Application-independent feature selection for texture classification
Pattern Recognition
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Texture is an important visual feature for computer vision tasks. In applications such as image retrieval and computer image understanding, texture similarity should be measured in a manner that is invariant to texture scale and orientation, as well as consistent with human's perception. However, most existing computational features and similarity measures are not perceptually consistent. A solution is to map textures into an invariant and perceptual space such that similarity measured in the space is perceptually consistent. This paper presents a hybrid method, using convolutional neural network and SVM, to perform the invariant and perceptual mapping. Test results show that it's overall performance is better than that of an individual neural network and a SVM.