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TOP-SIFT: the selected SIFT descriptor based on dictionary learning

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Abstract

The large amount of SIFT descriptors in an image and the high dimensionality of SIFT descriptor have made problems for the large-scale image database in terms of speed and scalability. In this paper, we present a descriptor selection algorithm based on dictionary learning to remove the redundant features and reserve only a small set of features, which we refer to as TOP-SIFTs. During the experiment, we discovered the inner relativity between the problem of descriptor selection and dictionary learning in sparse representation, and then turned our problem into dictionary learning. We designed a new dictionary learning method to adapt our problem and employed the simulated annealing algorithm to obtain the optimal solution. During the process of learning, we added the sparsity constraint and spatial distribution characteristic of SIFT points. And lastly selected the small representative feature set with good spatial distribution. Compared with the earlier methods, our method is neither relying on the database nor losing important information, and the experiments have shown that our algorithm can save memory space a lot and increase time efficiency while maintaining the accuracy as well.

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Acknowledgements

This work is partly supported by National Natural Science Foundation of China (Grant Nos. 61379106, 61379082, 61227802) and the Shandong Provincial Natural Science Foundation (Grant Nos. ZR2013FM036, ZR2015FM011, ZR2015FM022).

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Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, China

    Yujie Liu, Deng Yu, Xiaoming Chen & Zongmin Li

  2. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, USA

    Jianping Fan

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  1. Yujie Liu
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  2. Deng Yu
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  3. Xiaoming Chen
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  4. Zongmin Li
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  5. Jianping Fan
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Correspondence to Deng Yu.

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Liu, Y., Yu, D., Chen, X. et al. TOP-SIFT: the selected SIFT descriptor based on dictionary learning. Vis Comput 35, 667–677 (2019). https://doi.org/10.1007/s00371-018-1502-1

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