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Instance Retrieval Using Region of Interest Based CNN Features

Jingcheng Chen1, Zhili Zhou1,2,*, Zhaoqing Pan1, Ching-nung Yang3

Jiangsu Engineering Center of Network Monitoring & Department of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Department of Electrical and computer Engineer, University of Winsor, N9B3P4, Winsor, ON, Canada.
Department of Computer Science and Information Engineering National Dong Hwa University, Shoufeng, Hualien 974, Taiwan.

*Corresponding Author: Zhili Zhou. Email: email.

Journal of New Media 2019, 1(2), 87-99. https://doi.org/10.32604/jnm.2019.06582

Abstract

Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that the visual matching can be implemented at image region-level to effectively identify target objects from cluttered backgrounds. Moreover, we test the performance of the proposed RoI-based CNN feature, when it is extracted from different convolutional layers or fully-connected layers. Also, we compare the performance of RoI-based CNN feature with those of the state-of-the-art CNN features on two instance retrieval benchmarks. Experimental results show that the proposed RoI-based CNN feature provides superior performance than the state-of-the-art CNN features for in-stance retrieval.

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APA Style
Chen, J., Zhou, Z., Pan, Z., Yang, C. (2019). Instance retrieval using region of interest based CNN features. Journal of New Media, 1(2), 87-99. https://doi.org/10.32604/jnm.2019.06582
Vancouver Style
Chen J, Zhou Z, Pan Z, Yang C. Instance retrieval using region of interest based CNN features. J New Media . 2019;1(2):87-99 https://doi.org/10.32604/jnm.2019.06582
IEEE Style
J. Chen, Z. Zhou, Z. Pan, and C. Yang "Instance Retrieval Using Region of Interest Based CNN Features," J. New Media , vol. 1, no. 2, pp. 87-99. 2019. https://doi.org/10.32604/jnm.2019.06582

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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