Abstract
Scene classification is an important issue in the computer vision field. In this paper, we propose an improved approach for scene classification. Compared with the previous work, the proposed approach has two processes to improve the performance of scene classification. First, to represent scene images more effectively, feature combination is conducted to extract more effective information to describe characteristics of each category decreasing the influence of scale, rotation and illumination. Second, to extract more discriminative information for building a multi-category classifier, a kernel fusion method is proposed. Experimental results on four commonly used data sets show that the use of the feature and kernel combination method can improve the classification accuracy effectively.
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