An Effective Texture Image Segmentation Approach and Parameter Selection Effects Based on Sparse Coding
Sparse coding theory was an effective method for finding a compact representation of multidimensional data. In this paper, its application in the field of texture images analysis by means of Independent Component Analysis (ICA) is discussed. First, a bank of basis vectors was trained from a set of training images according to it. And the optimal texture features were selected from original ones which are extracted by convolving the test image with those basis vectors. Then the probabilities of these selected features were modeled by Gaussian Mixture Model (GMM). And final segmentation result was obtained after applying Expectation Maximization (EM) algorithm for clustering. Finally, a short discussion of the effects of different parameters (window size, feature dimensions, etc.) was given. Furthermore, combing the optimal texture features collected by ICA with the color features of the natural images, the proposed method was used in color image segmentation. The experimental results demonstrate that the proposed segmentation method based on sparse coding theory can archive promising performance.
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Document Type: Research Article
Publication date: March 1, 2012
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