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Information 2018, 9(2), 38;

Local Patch Vectors Encoded by Fisher Vectors for Image Classification

Institute of Intelligence Science and Technology, Hohai University, Nanjing 211100, China
School of Information Science and Technology, Yancheng Teachers University, Yancheng 224002, China
College of Education, Anqing Normal University, Anqing 246133, China
Department of Mathematics and Physics, Hefei University, Hefei 230601, China
Author to whom correspondence should be addressed.
Received: 22 December 2017 / Revised: 30 January 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
(This article belongs to the Section Artificial Intelligence)
PDF [3029 KB, uploaded 9 February 2018]


The objective of this work is image classification, whose purpose is to group images into corresponding semantic categories. Four contributions are made as follows: (i) For computational simplicity and efficiency, we directly adopt raw image patch vectors as local descriptors encoded by Fisher vector (FV) subsequently; (ii) For obtaining representative local features within the FV encoding framework, we compare and analyze three typical sampling strategies: random sampling, saliency-based sampling and dense sampling; (iii) In order to embed both global and local spatial information into local features, we construct an improved spatial geometry structure which shows good performance; (iv) For reducing the storage and CPU costs of high dimensional vectors, we adopt a new feature selection method based on supervised mutual information (MI), which chooses features by an importance sorting algorithm. We report experimental results on dataset STL-10. It shows very promising performance with this simple and efficient framework compared to conventional methods. View Full-Text
Keywords: image classification; fisher vector; mutual information; feature selection image classification; fisher vector; mutual information; feature selection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chen, S.; Liu, H.; Zeng, X.; Qian, S.; Wei, W.; Wu, G.; Duan, B. Local Patch Vectors Encoded by Fisher Vectors for Image Classification. Information 2018, 9, 38.

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