Using binarization and hashing for efficient SIFT matching |
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| Affiliation: | 1. Department of Computer Science and Engineering, Tatung University, No.40, Sec. 3, Zhongshan N. Rd., Taipei City 10452, Taiwan, ROC;2. Taipei College of Maritime Technology, New Taipei City 25172, Taiwan, ROC;1. Department of Electrical Engineering, National Chung Cheng University, Chia-Yi, Taiwan, ROC;2. Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Chia-Yi, Taiwan, ROC;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China;1. Department of Electronic Engineering, National Ilan University, Yilan 26047, Taiwan;2. Department of Information Management, St. Mary’s Junior College of Medicine, Nursing and Management, Yilan 26644, Taiwan;1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Technology, Xiamen University, Xiamen 361005, China;2. Cognitive Science Department, Xiamen University, Xiamen 361005, China;3. Computer Science Department, Xiamen University, Xiamen 361005, China |
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| Abstract: | The well-known SIFT is capable of extracting distinctive features for image retrieval. However, its matching is time consuming and slows down the entire process. In the SIFT matching, the Euclidean distance is used to measure the similarity of two features, which is expensive because it involves taking square root. Moreover, the scale of the image database is usually too large to adopt linear search for image retrieval. To improve the SIFT matching, this paper proposes a fast image retrieval scheme transforms the SIFT features to binary representations. The complexity of the distance calculation is reduced to bit-wise operation and the retrieval time is greatly decreased. Moreover, the proposed scheme utilizes hashing for retrieving similar images according to the binarized features and further speeds up the retrieval process. The experiment results show the proposed scheme can retrieve images efficiently with only a little sacrifice of accuracy as compared to SIFT. |
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| Keywords: | Image retrieval Image hashing SIFT feature Feature extraction Feature binarization Binary descriptor Feature matching Hashing |
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