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Optimized learning instance-based image retrieval

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Abstract

Image retrieval is a recognition technique in the field of computer vision. In most cases, high-quality retrieval is often supported by adequate learning instances. However, in the process of learning instance selection, some useless, repeated, invalid, and even mistaken learning instances are often selected. Low-quality instances not only add to the computing burden but also decrease the retrieval quality. In this study, we propose a learning instance optimization method. Initially, we classify the images into scene and object images by using the K-means clustering model. We use different methods to handle these two groups of images. For scene images, we use the Euclidean distance of the GIST descriptor to select the optimized learning instances. For object images, we use the improved spatial pyramid matching and optimal instance distance methods to select the optimized learning instances. Finally, we implement experiments using one large image database to check the effectiveness of our proposed algorithm. Results show that our method can not only improve retrieval quality but also decrease the number of learning instances.

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Acknowledgements

This research is sponsored by National Natural Science Foundation of China (Nos.61601033,61371185, 61401029, 61571049), China Postdoctoral Science Foundation(212400201, 2016M591109), the Fundamental Research Funds for the Central Universities (Nos. 2014KJJCB32, 2013NT57, 2012LYB46), Research Funds (15ZR003) and by SRF for ROCS, SEM, NSFC61273274, 61370127 and 61201158,NSFB4123104, FRFCU 2014JBZ004, Z131110001913143.

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Authors and Affiliations

  1. College of Information Science & Technology, Hebei Agricultural University, Baoding, China

    Yueli Li

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

    Rongfang Bie & Hao Wu

  3. Standards & Metrology Research Institute of CARS, Beijing, China

    Chenyun Zhang

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

    Zhenjiang Miao, Yuqi Wang & Hao Wu

  5. No.1 Senior Middle School of Wendeng District, Weihai, China

    Jiajing Wang

  6. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Hao Wu

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  1. Yueli Li
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  2. Rongfang Bie
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  4. Zhenjiang Miao
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  5. Yuqi Wang
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  6. Jiajing Wang
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  7. Hao Wu
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Correspondence to Hao Wu.

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Li, Y., Bie, R., Zhang, C. et al. Optimized learning instance-based image retrieval. Multimed Tools Appl 76, 16749–16766 (2017). https://doi.org/10.1007/s11042-016-3950-9

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  • DOI: https://doi.org/10.1007/s11042-016-3950-9

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