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An image similarity descriptor for classification tasks
Affiliation:1. Department of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, South Korea;2. Engineering Research Center on Cloud Computing & Internet of Things and E-commerce Intelligence of Fujian Universities Quanzhou Normal University, No. 398, Donghai Street, Fengze District, Quanzhou 362000, China;3. School of Economics and Management, Xinyu University, No. 2666, Yangguang Street, Xinyu 338004, China;1. School of Architecture, South China University of Technology, Guangzhou 510641, China;2. Foreign Language Teaching Department, Guang Zhou Vocational School of Finance and Economics, Guang Zhou 510080, China;3. School of Financial Mathematics and Statistics, GuangDong University of Finance, Guangzhou 510521, China;1. Academy for Engineering and Technology, Fudan University, Shanghai 200433, China;2. Shanghai Engineering Research Center of AI & Robotics, China;3. Engineering Research Center of AI & Robotics, Ministry of Education, China;4. School of Information Science and Technology, Fudan University, Shanghai 200433, China;5. Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA;1. Department of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan;2. Department of Communication Engineering, National Central University, Taoyuan City 320, Taiwan;1. UFSCar - Federal University of São Carlos, Department of Computing, São Carlos, Brazil;2. UNESP - São Paulo State University, School of Sciences, Bauru, Brazil;3. UNESP - São Paulo State University, School of Sciences, Bauru, Brazil;4. Ostbayerische Technische Hochschule, Regensburg, Germany;5. UNICAMP - University of Campinas, Institute of Computing, Campinas, Brazil
Abstract:We develop an image similarity descriptor for an image pair, based on deep features. The development consists of two parts - selecting the deep layer whose features are to be included in the descriptor, and a representation of the similarity between the images in the pair. The selection of the deep layer follows a sparse representation of the feature maps followed by multi-output support vector regression. The similarity representation is based on a novel correlation between the histograms of the feature maps of the two images. Experiments to demonstrate the effectiveness of the proposed descriptor are carried out on four applications that can be cast as classification tasks.
Keywords:Image similarity  Similarity representation  Deep features selection  Correlational descriptor
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