Abstract
Recent object detection systems utilize contextual information to boost recognition performance. A state-of-the-art contextual object detection method [7] adopts a structural model with greedy forward search inference algorithm. In this paper, we propose an isolation method for contextual object detection. It decomposes a complicated structural learning problem into several “local” ones, which can be efficiently solved by standard SVMs, to boost the speed of training and inference processes. Moreover, such isolation can readily deal with additional real-valued features to further improve the performance. The experimental results on PASCAL VOC 2007 dataset demonstrate the superiority of our method relative to other state-of-the-art ones both in computational cost and detection accuracy.
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Zhu, Y., Zhu, J., Zhang, R. (2012). An Efficient Isolation Method for Contextual Object Detection. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_29
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DOI: https://doi.org/10.1007/978-3-642-34595-1_29
Publisher Name: Springer, Berlin, Heidelberg
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