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Training Strategy of Semantic Concept Detectors Using Support Vector Machine in Naked Image Classification

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Advances in Multimedia Information Processing - PCM 2010 (PCM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6297))

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Recently, in the Web and online social networking sites, the classification and filtering for naked images have been receiving a significant amount of attention. In our previous work, semantic feature in the aforementioned application is found to be more useful compared to using only low-level visual feature. In this paper, we further investigate the effective training strategy when making use of Support Vector Machine (SVM) for the purpose of generating semantic concept detectors. The proposed training strategy aims at increasing the performances of semantic concept detectors by boosting the ’naked’ image classification performance. Extensive and comparative experiments have been carried out to access the effectiveness of proposed training strategy. In our experiments, each of the semantic concept detectors is trained with 600 images and tested with 300 images. In addition, 3 data sets comprising of 600 training images and 1000 testing images are used to test the naked image classification performance. The experimental results show that the proposed training strategy allows for improving semantic concept detection performance compared to conventional training strategy in use of SVM. In addition, by using our training strategy, one can improve the overall naked image classification performance when making use of semantic features.

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Jeon, J., Choi, J.Y., Kim, S., Min, H., Han, S., Ro, Y.M. (2010). Training Strategy of Semantic Concept Detectors Using Support Vector Machine in Naked Image Classification . In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15701-1

  • Online ISBN: 978-3-642-15702-8

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