Abstract
Co-occurrence features are effective for object classification because observing co-occurrence of two events is far more informative than observing occurrence of each event separately. For example, a color co-occurrence histogram captures co-occurrence of pairs of colors at a given distance while a color histogram just expresses frequency of each color. As one of such co-occurrence features, CoHOG (co-occurrence histograms of oriented gradients) has been proposed and a method using CoHOG with a linear classifier has shown a comparable performance with state-of-the-art pedestrian detection methods. According to recent studies, it has been suggested that combining heterogeneous features such as texture, shape, and color is useful for object classification. Therefore, we introduce three heterogeneous features based on co-occurrence called color-CoHOG, CoHED, and CoHD, respectively. Each heterogeneous features are evaluated on the INRIA person dataset and the Oxford 17/102 category flower datasets. The experimental results show that color-CoHOG is effective for the INRIA person dataset and CoHED is effective for the Oxford flower datasets. By combining above heterogeneous features, the proposed method achieves comparable classification performance to state-of-the-art methods on the above datasets. The results suggest that the proposed method using heterogeneous features can be used as an off-the-shelf method for various object classification tasks.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Dollár, P., Babenko, B., Belongie, S., Perona, P., Tu, Z.: Multiple component learning for object detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 211–224. Springer, Heidelberg (2008)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Machine Learning Research 9, 1871–1874 (2008)
Gehler, P.V., Nowozin, S.: On feature combination for multiclass object classification. In: Proceedings of the Twelfth IEEE International Conference on Computer Vision (2009)
Granlund, G.H.: In search of a general picture processing operator. In: Computer Graphics and Image Processing, pp. 155–173 (1978)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, p. 762. IEEE Computer Society, Washington (1997)
Koschan, A.: A comparative study on color edge detection. In: Proceedings of the 2nd Asian Conference on Computer Vision, pp. 574–578 (1995)
Kozakaya, T., Ito, S., Kubota, S., Yamaguchi, O.: Cat face detection with two heterogeneous features. In: Proceedings of the 2009 IEEE International Conference on Image Processing (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)
Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1447–1454 (2006)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (December 2008)
Ott, P., Everingham, M.: Implicit color segmentation features for pedestrian and object detection. In: Proceedings of the Twelfth IEEE International Conference on Computer Vision (2009)
Rautkorpi, R., Iivarinen, J.: A novel shape feature for image classification and retrieval. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004, Part I. LNCS, vol. 3211, pp. 753–760. Springer, Heidelberg (2004)
Ruzon, M.A., Tomasi, C.: Color edge detection with the compass operator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 160–166 (1999)
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Schwartz, W.R., Kembhavi, A., Harwood, D., Davis, L.S.: Human detection using partial least squares analysis. In: Proceedings of the Twelfth IEEE International Conference on Computer Vision (2009)
Swain, M.J., Ballard, D.H.: Color indexing. Int. Journal of Computer Vision 7(1), 11–32 (1991)
Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007)
Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for pedestrian detection. In: The 3rd Pacific Rim Symposium on Advances in Image and Video Technology, pp. 37–47 (2009)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: The Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 90–97. IEEE Computer Society, Washington (2005)
Wu, B., Nevatia, R.: Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)
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Ito, S., Kubota, S. (2010). Object Classification Using Heterogeneous Co-occurrence Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_51
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DOI: https://doi.org/10.1007/978-3-642-15555-0_51
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