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A Hybrid Bags-of-Feature model for Sports Scene Classification

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Abstract

In this paper, we propose an effective framework for sports scene classification by using a Hybrid Bags-of-Feature model. The Bag-of-Feature (BoF) model is a methodology which represents an image based on the histogram of visual codewords. With its successful performance, the BoF model has been widely exploited in computer vision such as scene classification and object recognition. Unlike traditional BoF models which resort to a single feature descriptor such as SIFT and thus exploit a single codebook, the proposed Hybrid BoF model employs two different types of codebooks; SIFT feature and modified LBP feature. The LBP descriptor is capable of capturing micro patterns of the image, hence it is suitable for texture classification. In contrast, Gradient based feature descriptors such as SIFT have been proven to be effective for object recognition. More specifically, foreground objects (or regions containing distinct silhouettes) are encoded based on the SIFT descriptor and the remaining regions such as sky, lawn or floor of the gymnasium are encoded based on the LBP descriptor. To build a criterion for descriptor selection, we introduce a saliency pyramid based on the Phase Fourier Transform (PFT). The proposed method has been extensively tested, and experimental results show that the proposed framework is effective for sports scene classification compared to other various state-of-the-art methods.

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References

  1. Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3), 145–175.

    Article  Google Scholar 

  2. Fei-Fei, L., Perona, P., 2005. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of IEEE computer vision and pattern recognition (CVPR) (pp. 524–531).

  3. Lazebnik, S., Schmid, C., Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 2169–2178).

  4. Szummer, M., & Picard, R.W. (1998). Indoor-outdoor image classification. In Proceedings IEEE International Workshop on Content-Based Access of Image and Video Database (pp. 42–51).

  5. Duan, L.Y., Xu, M., Tian, Q., Xu, C.S., Jin, J.S. (2005). A unified framework for semantic shot classification in sports video. IEEE Transactions on Multimedia, 7(6), 1066–1083.

    Article  Google Scholar 

  6. Zhu, G., Xu, C., Huang, Q., Rui, Y., Jiang, S., Gao, W., Yao, H. (2009). Event tactic analysis based on broadcast sports video. IEEE Transactions on Multimedia, 11(1), 49–67.

    Article  Google Scholar 

  7. Yang, Y., Yu, K., Gong, Y., Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 1794–1801).

  8. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y. (2010). Locality-constrained Linear Coding for image classification. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 3360–3367).

  9. Liu, L., Wang, L., Liu, X. (2011). In defense of soft-assignment coding. In Proceedings of IEEE International Conference on Computer Vision (ICCV) (pp. 2486–2493).

  10. Dorko, G., & Schmid, C. (2005). Object class recognition using discriminative local features. Technical Report, RR-5497, INRIA-Rhone-Alpes.

  11. Wu, J., & Rehg, J.M. (2009). Beyond the Euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In Proceedings of IEEE International Conference on Computer Vision (ICCV) (pp. 630–637).

  12. Wang, L. (2007). Toward A Discriminative Codebook: Codeword Selection across Multi-resolution. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 1–8).

  13. Ojala, T., Pietikäinen, M., Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  Google Scholar 

  14. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), 1, 886–893.

    Google Scholar 

  15. Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.

    Article  Google Scholar 

  16. Ojala, T., Pietikäinen, M., Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51–59.

    Article  Google Scholar 

  17. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645.

    Article  Google Scholar 

  18. Guo, C., Ma, Q., Zhang, L. (2008). Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In Proceedings of IEEE computer vision and pattern recognition (CVPR) (pp. 1–8).

  19. Wu, J., & Rehg, J.M. (2011). CENTRIST: A visual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1489–1501.

    Article  Google Scholar 

  20. Chu, W.T., & Chen, C. H. (2012). Color CENTRIST: a color descriptor for scene categorization. In Proceedings of ACM International Conference on Multimedia Retrieval (ICMR) (p. 33).

  21. Salton, G., & McGill, M. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.

    Google Scholar 

  22. Zhang, W., Surve, A., Fern, X., Dietterich, T. (2009). Learning non-redundant codebooks for classifying complex objects. In Proceedings of International Conference on Machine Learning (ICML) (pp. 1241–1248).

  23. Li, L.J., & Fei-Fei, L. (2007). What, where and who? Classifying events by scene and object recognition. In Proceedings of IEEE International Conference on Computer Vision (ICCV) (pp. 1–8).

  24. Wang, C., Blei, D., Fei-Fei, L. (2009). Simultaneous image classification and annotation. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 1903–1910).

  25. Li, L.J., Su, H., Xing, E.P., Fei-Fei, L. (2010). Object bank: a high-level image representation for scene classification and semantic feature sparsification. In Proceedings of Advances in Neural Information Processing Systems (NIPS), vol. 24.

  26. Wang, J., Li, Y., Zhang, Y., Xie, H., Wang, C. (2011). Boosted learning of visual word weighting factors for Bag-of-Features based medical image retrieval. In Proceedings of International Conference on Image and Graphics (ICIG) (pp. 1035–1040).

  27. Oppenheim, A.V., & Lim, J.S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69(5), 529–541.

    Article  Google Scholar 

  28. Hou, X., & Zhang, L. (2007). Saliency detection: A spectral residual approach. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), (pp. 1–8).

  29. Aiger, D., & Talbot, H. (2010). The Phase Only Transform for unsupervised surface defect detection. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 295–302).

  30. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9, 62–66.

    Article  Google Scholar 

  31. Guo, C., & Zhang, L. (2010). A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Transactions on Image Processing, 19(1), 185–198.

    Article  MathSciNet  Google Scholar 

  32. Kim, W., Jung, C., Kim, C. (2011). Spatiotemporal saliency detection and its applications in static and dynamic scenes. IEEE Transactions on Circuits and Systems for Video Technology, 21(4), 446–456.

    Article  MathSciNet  Google Scholar 

  33. Ojala, T., Pietikäinen, M., Harwood, D. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1 (pp. 582–585).

  34. Ahonen, T., Hadid, A., Pietikäinen, M. (2004). Face recognition with local binary patterns. In Proceedings of European Conference on Computer Vision (ECCV) (pp. 469–481).

  35. Gunay, A., & Nabiyev, V.V. (2008). Automatic age classification with LBP. In Proceedings of International Symposium on Computer and Information Sciences (pp. 1–4).

  36. Niu, Z., Hua, G., Gao, X., Tian, Q. (2012). Context aware topic model for scene recognition. In Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR) (pp. 2743–2750).

  37. Morioka, N., & Satoh, S. (2010). Building compact local pairwise codebook with joint feature space clustering. In Proceedings European Conference on Computer Vision (ECCV) (pp. 692–705).

  38. Chang, C.C., & Lin, C.J. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of EE, KAIST, Daejeon, Korea

    Dong Jin Park & Changick Kim

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  1. Dong Jin Park
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  2. Changick Kim
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Correspondence to Dong Jin Park.

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Park, D.J., Kim, C. A Hybrid Bags-of-Feature model for Sports Scene Classification. J Sign Process Syst 81, 249–263 (2015). https://doi.org/10.1007/s11265-014-0946-4

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  • DOI: https://doi.org/10.1007/s11265-014-0946-4

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