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Image Classification on Visual Dictionary Optimization

There is the fact that the traditional k-means clustering training dictionary method has the problems of redundancy, high time complexity and easy to fall into local convergence, especially dictionary training has a direct influence on image classification effect. An improvement of the dictionary training method was proposed by this paper aiming to get the most optimal visual dictionary quickly. Firstly, an improved k-means clustering is presented by changing method of center selection. Then use it to train independent dictionaries for each category, and these dictionaries were all optimized with Word Activation Forces (WAFs). Finally, all categories of the dictionary are integrated to one dictionary. The optimized dictionary was obtained by using WAFs to optimize once again. The proposed method was verified on the classic image databases Caltech101 and Caltech256. The results showed that it improved not only the efficiency, but also the accuracy of image classification.