[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Comparative Study of the Objectionable Video Classification Approaches Using Single and Group Frame Features

  • Conference paper
Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Included in the following conference series:

Abstract

This paper deals with the methods for classifying whether a video is harmful or not and also evaluates their performance. The objectionable video classification can be performed using two methods. One can be practiced by judging whether each frame included in the video is harmful, and the other be obtained by using the features reflecting the entire characteristics of the video. The former is a single frame-based feature and the latter is a group frame-based feature. Experimental results show that the group frame-based feature outperforms the single frame-based feature and is robust to the objectionable video classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Fleck, M., Forsyth, D., Bregler, C.: Finding Naked People. In: European Conf. on Computer Vision, vol. 2, pp. 592–602 (1996)

    Google Scholar 

  2. Jones, M.J., Regh, J.M.: Statistical Color Model with Application to Skin Detection. Technical Report CRL (1998)

    Google Scholar 

  3. Wang, J.Z., Wiederhold, G., Firschein, O.: System for Screening Objectionable Imagers. Computer Communications 21, 1355–1600 (1998)

    Article  Google Scholar 

  4. Bosson, A., Cawley, G.C., Chan, Y., Harvey, R.: Non-Retrieval: Blocking Pornographic Images. In: International Conf. on Image and Video Retrieval (2002)

    Google Scholar 

  5. Lee, J.-H.: Automatic Video Management System Using Face Recognition and MPEG-7 Visual Descriptors. ETRI Journal 27, 806–809 (2005)

    Article  Google Scholar 

  6. Jeong, C.-Y., Kim, J.-S., Hong, K.-S.: Appearance-Based Nude Image Detection. In: ICPR, pp. 467–470 (2004)

    Google Scholar 

  7. Tsishkou, D., Hammami, M., Chen, L.: Face Detection in Video Using Combined Data-mining and Histogram based Skin-color Model. In: Proceedings of 3rd International Symposium on Image and Signal Processing and Analysis, pp. 500–503 (2003)

    Google Scholar 

  8. Ikeda, O.: Segmentation of Face in Video Footage Using HSV Color for Face Detection and Image Retrieval. In: Image Processing, ICIP 2003, Proceedings, vol. 3, pp. 14–17 (2003)

    Google Scholar 

  9. Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7. John Wiley & Sons, Ltd., Chichester (2002)

    Google Scholar 

  10. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  11. Joachims, T.: SVMlight Support Vector Machine, http://svmlight.joachims.org/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, S., Lee, H., Nam, T. (2006). A Comparative Study of the Objectionable Video Classification Approaches Using Single and Group Frame Features. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_64

Download citation

  • DOI: https://doi.org/10.1007/11840930_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics