Image Classification Between Two Animals
DOI:
https://doi.org/10.32628/CSEIT2174103Keywords:
Classification, Classification of Cats and Dogs, Cats and Dogs.Abstract
We are going to examine the fine-grained object categorization problem of identifying the breed of animal from a picture. To this end we introduce a replacement annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is extremely challenging for the cat and dog, particularly cats, are very deformable and there are often exactly subtle differences between their breeds. We make variety of contributions: we first introduce a model to classify cat and dog breed automatically from a picture. The model adding the shape of the pet animals, captured by a deformable part model detecting the cat and dog face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the cat and dog within the image. Second, we compare two classification approaches: a hierarchical one, during which a pet animal is first assigned to the cat or dog family then to a breed, and a flat one, during which the breed is obtained directly.
References
- S. Branson, C. Wah, F. Schroff, B. Babenko, P. Welinder, P. Perona, and S. Belongie. Visual recognition with humans in the loop. In Proc. ECCV, 2010.
- Y. Chai, V. Lempitsky, and A. Zisserman. Bicos: A bi-level co-segmentation method for image classification. In Proc. ICCV, 2011.
- G. Csurka, C. R. Dance, L. Dan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Proc. ECCV Workshop on Stat. Learn. in Comp. Vision, 2004.
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005. 18J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In Proc. CVPR, 2009.
- J. Elson, J. Douceur, J. Howell, and J. J. Saul. Asirra: A CAPTCHA that exploits interest-aligned manual image categorization. In Conf. on Computer and Communications Security (CCS), 2007.
- M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results. http://www.pascalnetwork.org/challenges/VOC/voc2011/workshop/index.html.
- L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proc. ICCV, 2003.
- P. F. Felzenszwalb, R. B. Grishick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 2009.
- F. Fleuret and D. Geman. Stationary features and cat detection. Journal of Machine Learning Research, 9, 2008.
- P. Golle. Machine learning attacks against the asirra captcha. In 15th ACM Conference on Computer and Communications Security (CCS), 2008.
- G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical report, California Institute of Technology, 2007.
- A. Khosla, N. Jayadevaprakash, B. Yao, and F. F. Li. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, CVPR, 2011.
- C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In Proc. CVPR, 2009.
- I. Laptev. Improvements of object detection using boosted histograms. In Proc. BMVC, 2006.
- Golle, P. (2008, October). Machine learning attacks against the Asirra CAPTCHA. In Proceedings of the 15th ACM conference on Computer and communications security (pp. 535-542). ACM.
- J. Elson, J. Douceur, J. Howell and J. Saul. Asirra: a CAPTCHA that exploits interest-aligned
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