Bloody Image Classification with Global and Local Features

  • Song-Lu Chen
  • Chun Yang
  • Chao Zhu
  • Xu-Cheng YinEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 663)


Object content understanding in images and videos draws more and more attention nowadays. However, only few existing methods have addressed the problem of bloody scene detection in images. Along with the widespread popularity of the Internet, violent contents have affected our daily life. In this paper, we propose region-based techniques to identify a color image being bloody or not. Firstly, we have established a new dataset containing 25431 bloody images and 25431 non-bloody images. These annotated images are derived from the Violent Scenes Dataset, a public shared dataset for violent scenes detection in Hollywood movies and web videos. Secondly, we design a bloody image classification method with global visual features using Support Vector Machines. Thirdly, we also construct a novel bloody region identification approach using Convolutional Neural Networks. Finally, comparative experiments show that bloody image classification with local features is more effective.


Bloody image classification Violent scenes dataset Support vector machines Convolutional Neural Networks 


  1. 1.
    Ying-Wei, W., et al.: Bloodstain segmentation in color images. In: Proceedings of the 2011 First International Conference on Robot, Vision and Signal Processing (RVSP 2011), pp. 52–55 (2011)Google Scholar
  2. 2.
    de Dios, J.J., et al.: Face detection based on a new color space YCgCr. In: Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 902–912, September 2003Google Scholar
  3. 3.
    de Dios, J.J., Garcia, N.: Fast face segmentation in component color space. In: 2004 International Conference on Image Processing (ICIP) (IEEE Cat. No. 04CH37580), vol. 191, pp. 191–194 (2004)Google Scholar
  4. 4.
    Yoo, S., Park, R.H.: Red-Eye detection and correction using inpainting in digital photographs. IEEE Trans. Consum. Electron. 55(3), 1006–1014 (2009)CrossRefGoogle Scholar
  5. 5.
    Yan, G., et al.: Region-based blood color detection and its application to bloody image filtering. In: Proceedings of 2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 45–50 (2015)Google Scholar
  6. 6.
    Wang, D., et al.: Baseline results for violence detection in still images. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 54–57 (2012)Google Scholar
  7. 7.
    Li, B., Hu, W., Xiong, W., Wu, O., Li, W.: Horror image recognition based on emotional attention. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6493, pp. 594–605. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19309-5_46 CrossRefGoogle Scholar
  8. 8.
    Guermazi, R., et al.: Violent web images classification based on MPEG7 color descriptors. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 3106–3111 (2009)Google Scholar
  9. 9.
    Lopes, A.P.B., et al.: A bag-of-features approach based on Hue-SIFT descriptor for nude detection. In: 2009 17th European Signal Processing Conference (EUSIPCO 2009), pp. 1552–1556 (2009)Google Scholar
  10. 10.
    Ulges, A., et al.: Automatic detection of child pornography using color visual words. In: 2011 IEEE International Conference on Multimedia and Expo (2011)Google Scholar
  11. 11.
  12. 12.
    Demarty, C.H., et al.: VSD, a public dataset for the detection of violent scenes in movies: design, annotation, analysis and evaluation. Multimedia Tools Appl. 74(17), 7379–7404 (2015)CrossRefGoogle Scholar
  13. 13.
    Schedi, M., et al.: VSD2014: a dataset for violent scenes detection in Hollywood movies and web videos. In: 2015 Proceedings of 13th International Workshop on Content-Based Multimedia Indexing (CBMI), p. 6 (2015)Google Scholar
  14. 14.
    Acar, E., et al.: Detecting violent content in Hollywood movies by mid-level audio representations. In: Czuni, L., Schoffmann, K., Sziranyi, T. (eds.) 2013 11th International Workshop on Content-Based Multimedia Indexing, pp. 73–78 (2013)Google Scholar
  15. 15.
    Gong, Y., Wang, W., Jiang, S., Huang, Q., Gao, W.: Detecting violent scenes in movies by auditory and visual cues. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 317–326. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-89796-5_33 CrossRefGoogle Scholar
  16. 16.
    Penet, C., et al.: Multimodal information fusion and temporal integration for violence detection in movies. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2393–2396 (2012)Google Scholar
  17. 17.
    Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 332–339. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23678-5_39 CrossRefGoogle Scholar
  18. 18.
    Deniz, O., et al.: Fast violence detection in video. In: Proceedings of 9th International Conference on Computer Vision Theory and Applications (VISAP 2014), pp. 478–485 (2014)Google Scholar
  19. 19.
    Zhijie, Y., et al.: Violence detection based on histogram of optical flow orientation. In: Proceedings of the SPIE - The International Society for Optical Engineering (2013)Google Scholar
  20. 20.
    Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of IEEE ICCV, September 2009Google Scholar
  21. 21.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. In: ACM Trans. Intell. Syst. Technol. 2(3) (2011)Google Scholar
  22. 22.
    Xu-Cheng, Y., et al.: Shallow classification or deep learning: an experimental study. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1904–1909 (2014)Google Scholar
  23. 23.
    MatConvNet: CNNs for MATLAB. Accessed 25 May 2016

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Song-Lu Chen
    • 1
  • Chun Yang
    • 1
  • Chao Zhu
    • 1
  • Xu-Cheng Yin
    • 1
    Email author
  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations