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, 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 show that the use of the feature and kernel combination method can improve the classification accuracy effectively.
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Yuan, L., Chen, F., Zhou, L., Hu, D. (2013). Improve Scene Classification by Using Feature and Kernel Combination. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_17
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DOI: https://doi.org/10.1007/978-3-642-42057-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
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