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The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between images the optimization methods for Codebook based on interdependence between the image code word co-occurrence relationships. Experimental results show that the improved clustering algorithm and code optimization method proposed in this paper can help to establish a codebook with more ability in expression and discrimination, which can significantly improve the accuracy of image classification.

In this paper, we propose a method for automatic classifying fragments in computer-aided restoration of ceramic cultural relics, using surface texture clustering. Firstly, center of clustering will be initialized by re-division on multi-variant finite model. Then kernel-based fuzzy clustering algorithm is applied and parameters of difference degree are used to control iterations of clustering. The experiment result shows that this scheme is characterized by better automatic classifying rate for Ancient-Ceramic fragments.