Abstract
Aiming at recognizing ship target efficiently and accurately, a novel method based on active learning and the Composite Kernel Support Vector Machines (CK-SVM) is proposed. First, we build a ship recognition dataset which contains the major warship models and massive civil ships. Second, to reduce the cost of manual labeling, active learning algorithm is run to select the most informative and representative samples to label. Finally, we construct a composite-kernel SVM combining shape and texture features to recognize ships. The composite-kernel strategy can enhance the quality of features fusion apparently. Experiments demonstrate that our method not only improves the efficiency of samples selection, but also receives satisfying results.
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Pan, B., Jiang, Z., Wu, J., Zhang, H., Luo, P. (2015). Ship Recognition Based on Active Learning and Composite Kernel SVM. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_23
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DOI: https://doi.org/10.1007/978-3-662-47791-5_23
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