Search by item HOME > Access full text > Search by item

JBE, vol. 26, no. 2, pp.175-183, March, 2021


Comparison of Fine Grained Classification of Pet Images Using Image Processing and CNN

Jihae Kim, Jeonghwan Go, and Cheolhee Kwon

C.A E-mail:


The study of the fine grained classification of images continues to develop, but the study of object recognition for animals with polymorphic properties is proceeding slowly. Using only pet images corresponding to dogs and cats, this paper aims to compare methods using image processing and methods using deep learning among methods of classifying species of animals, which are fine grained classifications. In this paper, Grab-cut algorithm is used for object segmentation by method using image processing, and method using Fisher Vector for image encoding is proposed. Other methods used deep learning, which has achieved good results in various fields through machine learning, and among them, Convolutional Neural Network (CNN), which showed outstanding performance in image recognition, and Tensorflow, an open-source-based deep learning framework provided by Google. For each method proposed, 37 kinds of pet images, a total of 7,390 pages, were tested to verify and compare their effects.

Keyword: fine grained classification, object recognition, image processing, Convolutional Neural Network

[1] Lingxi Xie, Jingdong Wang, Bo Zhang, and Qi Tian, “Fine-Grained Image Search,” IEEE Transactions on multimedia, Vol. 17, No. 5, pp. 636-647, May 2015.
[2] C. Spampinato, S. Palazzo, P.H. Joalland, “Fine-Grained object recognition in underwater visual data,” Proceeding of the Springer Science+Business Media, New York, pp. 1-20, 2015.
[3] S Yu, Y Wu, W Li, Z Song, W Zeng, "A model for fine-grained vehicle classification based on deep learning," Neurocomputing, Vol. 257, pp. 97-103, 2017.
[4] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, C. V. Jawahar, "cats and dogs," IEEE Conference on Computer Vision and Pattern Recognition, pp. 3498-3505, 2012.
[5] C Rother, V Kolmogorov, A Blacke, “Grabcut: Interactive foreground extraction using iterated graph cuts,” ACM transactions on Graphics(TOG), Vol. 23, No. 3, pp. 309-314, August 2004.
[6] Yubing Li, Jinbo Zhang, Peng Gao, Liangcheng Jiang, Ming Chen, "Grab Cut Image Segmentation Based on Image Region," IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 311-315, 2018.
[7] C. Dance and F. Perronnin, “Fisher Kernels on Visual Vocabularies for Image Categorization,” Proceeding of the IEEE Conference on Computer Vision, Minneapolis, pp. 1-8, 2007.
[8] PH Gosselin, N Murray, H Jégou, F Perronnin, "Revisiting the fisher vector for fine-grained classification," Pattern recognition letters, Vol. 49, pp. 92-98, 2014.
[9] Gabriella Csurka, Christopher R.Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, “Visual categorization with bags of keypoints,” Proceeding of the workshop on statistical learning in Computer Vision, ECCV, pp. 1-16, 2004.
[10] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.V.Jawahar, “The Oxford-IIT Pet Dataset”, 2012.
[11] Tianmei Guo, Jiwen Dong, Henjian Li, Yunxing Gao, "Simple convolutional neural network on image classification," IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 721-724, 2017.
[12] Kiran Seetala, William Birdsong, Yenumula B. Reddy, "Image classification using Tensorflow," 16th International Conference on Information Technology-New Generations (ITNG 2019), pp. 485-488, 2019.


Editorial Office
1108, New building, 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Korea
Homepage: TEL: +82-2-568-3556 FAX: +82-2-568-3557
Copyrightⓒ 2012 The Korean Institute of Broadcast and Media Engineers
All Rights Reserved