Advertisement

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

  • Jaehyun Jeon
  • Jae Young Choi
  • Semin Kim
  • Hyunseok Min
  • Seungwan Han
  • Yong Man Ro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

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.

Keywords

Semantic concept detector naked image classification Support Vector Machine (SVM) training strategy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, J.S., Kuo, Y.-M., Chung, P.-C., Chen, E.-L.: Naked Image Detection based on Adaptive and Extensible Skin Color Model. Pattern Recognition 40, 2261–2270 (2007)zbMATHCrossRefGoogle Scholar
  2. 2.
    Shih, J.-L., Lee, C.-H., Yang, C.-S.: An Adult Image Identification System Employing Image Retrieval Technique. Pattern Recognition Letters 28, 2367–2374 (2007)CrossRefGoogle Scholar
  3. 3.
    Kim, W.I., Lee, H.-K., Yoo, S.J., Baik, S.W.: Neural Network based Adult Image Classification. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 481–486. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Kim, S.M., Min, H.S., Jeon, J.H., Ro, Y.M., Han, S.W.: Malicious Content Filtering based on Semantic Features. In: The ACM International Conference Proceeding (2009)Google Scholar
  5. 5.
    Yang, S.J., Kim, S.-K., Ro, Y.M.: Semantic Home Photo Categorization. IEEE Tran. On Circuits and Systems for Video Technology 17(3), 324–335 (2007)CrossRefGoogle Scholar
  6. 6.
    Boutell, M., Choudhury, A., Luo, J., Brown, C.M.: Using Semantic Features for Scene Classification: How Good do They Need to Be? In: IEEE International Conference on Multimedia and Expo, ICME (2006)Google Scholar
  7. 7.
    Naphade, M., Smith, J.R., Tesic, J., Chang, S.F., Hsu, W., Kennedy, L., Hauptmann, A., Curtis, J.: Large-scale Concept Ontology for Multimedia. IEEE Multimedia 13(3), 86–91 (2006)CrossRefGoogle Scholar
  8. 8.
    Li, X., Snoek, C.G.M.: Visual Categorization with Negative Examples for Free. In: The ACM Multimedia Conference, pp. 661–664 (2009)Google Scholar
  9. 9.
    Nilsson, R., Pena, J.M., Bjokegren, J., Tegner, J.: Evaluating Feature Selection for SVMs in High Dimensions. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 719–726. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature Mining for Image Classification. In: IEEE Conference on In Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)Google Scholar
  11. 11.
    Akbani, R., Kwek, S., Japkowicz, N.: Applying Support Vector Machines to Imbalanced Datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Snoek, C.G.M., Sande, K.E.A., Rooij, O., et al.: The MediaMill TRECVID 2008 Semantic Video Search Engine. In: Proceedings of TRECVID Workshop (2008)Google Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridege (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jaehyun Jeon
    • 1
  • Jae Young Choi
    • 1
  • Semin Kim
    • 1
  • Hyunseok Min
    • 1
  • Seungwan Han
    • 2
  • Yong Man Ro
    • 1
  1. 1.Image and Video Systems Lab, Department of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)Yuseong-guKorea
  2. 2.Knowledge-based Information Security & Safety Research DepartmentElectronics and Telecommunications Research InstituteYuseong-guKorea

Personalised recommendations