Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning
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ng Your Dog Breed: Identifying a Dog Breed with Deep Learning Punyanuch Borwarnginn Worapan Kusakunniran Sarattha Karnjanapreechakorn Kittikhun Thongkanchorn Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom 73170, Thailand
Abstract: Dog breed identification is essential for many reasons, particularly for understanding individual breeds′ conditions, health concerns, interaction behavior, and natural instinct. This paper presents a solution for identifying dog breeds using their images of their faces. The proposed method applies a deep learning based approach in order to recognize their breeds. The method begins with a transfer learning by retraining existing pre-trained convolutional neural networks (CNNs) on the public dog breed dataset. Then, the image augmentation with various settings is also applied on the training dataset, in order to improve the classification performance. The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons. The proposed model achieves a promising accuracy of 89.92% on the published dataset with 133 dog breeds. Keywords: Computer vision, deep learning, dog breed classification, transfer learning, image augmentation.
1 Introduction Image recognition and classification have successfully applied in various domains, such as face recognition[1, 2] and scene understanding for autonomous driving[3]. At present, human face identification is successfully used for authentication and security purposes in many applications. Therefore, there are attempts to extend studies from human to animal recognition. In particular, dogs are one of the most common animals. Since there are more than 180 dog breeds, dog breed recognition can be an essential task in order to provide proper training and health treatment. Previously, dog breed recognition is done by human experts. However, some dog breeds might be challenging to evaluate due to the lack of experts and the difficulty of breeds' patterns themselves. It also takes time for each evaluation. Besides, there are several studies on using dog images to identify their breeds. Chanvichitkul et al.[4] proposed using coarse to fine classification by grouping similar face contours as a coarse classification and then applying a principle component analysis (PCA) classifier within the output group as fine classification. Prasong et al.[5] extended the coarse to fine classification by adding local parts to reduce misclassification within the same group. This method used normalized cross correlation (NCC) to find each local part, such as ears and face. Then, the dog Research Article Manuscript received June 4, 2020; accepted September 30, 2020 Recommended by Associate Editor Matjaz Gams © Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2020
breeds were classified in the PCA subspaces. It improved the runtime by four times and yielded an
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