Abstract
In this paper we have proposed a new novel features model whichdesigned to robustly detect the highly variable cat head patterns.Do not like human, cats usually have distinct different face, pose,appearance and different scales of ears, eyes and mouth. So manysignificant features on human face detection have presented but itis not satisfying to use them on cat head. We have designed a newfeatures model by ideally combining the histogram frame withGLCM-based (gray level co-occurrence matrix) texture features todescribe both the shape information of cat’s head and textureinformation of cat’s eyes, ears and mouth in detail. SVM-basedclassifier achieves the detection results. Extensive experimentalresults illustrating the high detection rate with low false alarm.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients forhuman detection. In: CVPR, vol. 1, pp. 886–893 (2005)
Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR, vol. 2, pp. 1491–1498 (2006)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell. 20(1), 23–38 (1998)
Viola, P., Jones, M.J.: Robust real-time face detection. Intl. Journal of Computer Vision 57(2), 137–154 (2004)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR, vol. 1, pp. 878–885 (2005)
Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Machine Intell. 28, 1863–1868 (2006)
Papageorgiou, C., Poggio, T.: A trainable system forobject detection. Intl. J. Computer Vision 38, 15–33 (2000)
Schneiderman, H., Kanade, T.: A statistical method for 3dobject detection applied to faces and cars. In: CVPR, vol. 1, pp. 746–751 (2000)
Felzenszwalb, P.F.: Learning models for object recognition. In: CVPR, vol. 1, pp. 1056–1062 (2001)
Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: CVPR, vol. 1, pp. 87–93 (1999)
Lowe, D.G.: Object recognition from local scale-invariantfeatures. In: ICCV, vol. 2, pp. 1150–1157 (1999)
Levi, K., Weiss, Y.: Learning object detection from a smallnumber of examples: the importance of good features. In: CVPR, vol. 2, pp. 53–60 (2004)
Tuzel, O., Porikli, F., Meer, P.: Human detection viaclassification on Riemannian manifolds. In: CVPR (2007)
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learningshapelet features. In: CVPR (2007)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet partdetectors. In: ICCV, vol. 1, pp. 90–97 (2005)
Zhang, W., Sun, J., Tang, X.: Cat Head Detection- How to effectively exploit Shape and Texture Features. In: Proceedings of ECCV-European Conference on Computer Vision, pp. 802–816 (2008)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based objectdetection in images by components. PAMI 23, 349–361 (2001)
Schwartz, E.L.: Spatial mapping in the primate sensory projection:analytic structure and relevance to perception. Biological Cybernetics 25, 181–194 (1977)
Smith, J.R., Chang, S.F.: Automated binary texture feature setsfor image retrieval. In: Proc. of the IEEE International Conferenceon Acoustics, Speech, and Signal Processing, pp. 2239–2242 (1996)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture Features for Image Classification. IEEE Trans. on Systems, Man and Cybernetics 3, 610–621 (1973)
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Bo, H. (2010). A Novel Features Design Method for Cat Head Detection. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_47
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DOI: https://doi.org/10.1007/978-3-642-16530-6_47
Publisher Name: Springer, Berlin, Heidelberg
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