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Weighted-learning-instance-based retrieval model using instance distance

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Abstract

High-quality retrieval techniques can effectively retrieve target images from millions of images, and some classic techniques are widely used in different fields. As a classic image retrieval technique, deep learning shows remarkable advantages in significantly improving retrieval results. However, high-quality retrieval results highly depend on sufficient learning instances. When no sufficient learning instances exist to support learning model construction, then retrieval quality reduces remarkably. In most cases, sufficient learning instances lead to wasting of significant computing and human resources. Aiming at the aforementioned problem, we proposed a weighted-learning-instance-based retrieval model requiring instance distance calculation. Concretely, reference learning instance optimization, instance distance calculation, and innovative cost function construction are combined which could directly contribute to build up the previous model. Firstly, high-quality reference learning instances could be selected by learning instance optimization model. Then, combined with weights of learning instances calculated by instance distance, the innovative cost function could be constructed which could make full use of learning instances under various circumstances. More importantly, this model can significantly reduce the number of learning instances through instance optimization and weight definition while maintaining high level of retrieval quality. Adequate experimental results based on a large database show robustness and effectiveness of our model.

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Acknowledgements

This research is sponsored by National Natural Science Foundation of China (Nos. 61601033, 61571049, 61401029), Fundamental Research Funds for the Central Universities (No. 2016NT14), Beijing Municipal Natural Science Foundation (No. 9174027) and Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004).

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing Normal University, Beijing, China

    Hao Wu, Rongfang Bie & Junqi Guo

  2. College of Information Science and Technology, Hebei Agricultural University, Baoding, China

    Yueli Li

  3. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

    Jie Xiong

  4. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Xiaohan Bi

  5. College of Mechanical Engineering, Guizhou University, Guiyang, China

    Linna Zhang

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  1. Hao Wu
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Correspondence to Junqi Guo.

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Wu, H., Li, Y., Xiong, J. et al. Weighted-learning-instance-based retrieval model using instance distance. Machine Vision and Applications 30, 163–176 (2019). https://doi.org/10.1007/s00138-018-0988-x

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