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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: firstname.lastname@example.org
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
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