Feature Extraction And Clustering Ensemble Of Texture Image

With the continuous advancement of information technology, as intuitive and fast information media for human to understand the world, image occupy a very important position in the spread and development of culture. Texture image is a kind of special image, which is different from general image in analysis and description, due to its image distribution and the form of information transmission. How to classified different textures effectively has a great significance on storage, management and retrieval of image. This paper focuses on texture images clustering. The unsupervised feature of clustering brings convenience to massive data processing.Single clustering method is simple and convenient in the clustering of texture image features, but it always has low accuracy rate. Although integration of multiple clustering results can effectively compensate for this deficiency, existing clustering ensemble algorithm failed to keep a balance between computational complexity and accuracy rate. Before clustering, it is necessary to use a description method which has strong generality and lower dimension to reflect the texture features of image. CS-LBP has the advantages of lower computational complexity and better description ability, but still has some defects in human visual system and rotation problem.Base on the above analysis, first, we introduce fuzzy theory into the texture spectrum, and the FCS-LBP is designed to match the characteristic of human eye identification. We proposed DTSP (Dominant Texture Spectrum of Partition) to filter noise. Secondly, considering the disadvantages of existing CS-LBP in rotation robustness, we design ECS-LBP and SS(shift and summation method) to enhance the anti-rotation ability of texture spectrum. Finally, we put forward WVMC to optimize benchmark options of the voting-based clustering ensemble, and vote with weights, which are generated by the distances between cluster centers and data points in different cluster members. Because this method is simple and fast, it can be applied to large-scale data sets. Simulations demonstrate that, compared with CS-LBP, our two LBP methods have great improvement in clustering accuracy and rotation robustness respectively. Compared with traditional voting methods, our WVMC clustering ensemble method has higher clustering accuracy, lower time complexity and higher practicality.