Open Access

ARTICLE

Instance Retrieval Using Region of Interest Based CNN Features

Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³

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

*Corresponding Author: Zhili Zhou. 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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Citations