Machine-learning-based image categorization

In this paper, a novel and efficient automatic image categorization system is proposed. This system integrates the MIL-based and global-feature-based SVMs for categorization. The IN (Instance Prototypes) are derived from the segmented regions by applying MIL on the training images from different categories. The IPs-based image features are further used as inputs to a set of SVMs to find the optimum hyperplanes for categorizing training images. Similarly, global image features, including color histogram and edge histogram, are fed into another set of SVMs. For each test image, two sets of image features are constructed and sent to the two respective sets of SVMs. The decision values from two sets of SVMs are finally incorporated to obtain the final categorization results. The empirical results demonstrate that the proposed system outperforms the peer systems in terms of both efficiency and accuracy.