Jingcheng Chen1, Zhili Zhou1,2,*, Zhaoqing Pan1, Ching-nung Yang3
Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI: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 More >
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