Fine-grained image classification with factorized deep user click feature |
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| Institution: | 1. Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China;2. Department of Pain Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310018, China;3. Wuhan second ship design and research institute, Wuhan 430205, China;1. School of Information Management, Wuhan University, Wuhan, Hubei, China;2. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands;3. Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands;1. Bitlis Eren University, Informatics Department, 13000 Bitlis, Turkey;2. ?nönü University, Department of Computer Engineering, 44000 Malatya, Turkey;1. Dept. of Computing and Numerical Analysis University of Córdoba Córdoba, Spain;2. Maimonides Biomedical Research Institute of Cordoba (IMIBIC) Reina Sofia University Hospital, Córdoba, Spain;3. General and Digestive Surgery San Juan de Dios Hospital Córdoba, Spain;1. Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;2. King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;3. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;1. School of Information Management, Wuhan University, Wuhan 430072, PR China;2. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072, PR China |
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| Abstract: | The advantages of user click data greatly inspire its wide application in fine-grained image classification tasks. In previous click data based image classification approaches, each image is represented as a click frequency vector on a pre-defined query/word dictionary. However, this approach not only introduces high-dimensional issues, but also ignores the part of speech (POS) of a specific word as well as the word correlations. To address these issues, we devise the factorized deep click features to represent images. We first represent images as the factorized TF-IDF click feature vectors to discover word correlation, wherein several word dictionaries of different POS are constructed. Afterwards, we learn an end-to-end deep neural network on click feature tensors built on these factorized TF-IDF vectors. We evaluate our approach on the public Clickture-Dog dataset. It shows that: 1) the deep click feature learned on click tensor performs much better than traditional click frequency vectors; and 2) compared with many state-of-the-art textual representations, the proposed deep click feature is more discriminative and with higher classification accuracies. |
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