Abstract
To solve the low sampling efficiency problem of negative samples in object detection and information retrieval, a cascaded mixture SVM classifier along with its learning method is proposed in this paper. The classifier is constructed by cascading one-class SVC and two-class SVC. In the learning method, first, 1SVC is trained by using the cluster features of the positive samples, then the 1SVC trained is used to collect the negative samples close to the positive samples and to eliminate the outlier positive samples, finally, the 2SVC is trained by using the positive samples and effective negative samples collected. The cascaded mixture SVM classifier integrates the merits of both 1SVC and 2SVC, and has the characters of higher detection rate and lower false positive rate, and is suitable for object detection and information retrieval. Experimental results show that the cascaded SVM classifier outperforms traditional classifiers.
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Yuan, Z., Zheng, N., Liu, Y. (2005). A Cascaded Mixture SVM Classifier for Object Detection. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_145
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DOI: https://doi.org/10.1007/11427391_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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