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Proceedings Paper

Image classification based on region of interest detection
Author(s): Huabing Zhou; Yanduo Zhang; Zhenghong Yu
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Paper Abstract

For image classification tasks, the region containing object which plays a decisive role is indefinite in both position and scale. In this case, it does not seem quite appropriate to use the spatial pyramid matching (SPM) approach directly. In this paper, we describe an approach for handling this problem based on region of interest (ROI) detection. It verifies the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background for image classification. It first makes use of an object detection algorithm to separate an image into object and scene regions, and then constructs spatial histogram features for them separately based on SPM. Moreover, the detection score is used to rescore. Our contributions include: i) verify the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background used for image classification; ii) a simple method, called coarse object alignment matching, is proposed for constructing histogram using the foreground and background provided by object localization. Experimental results demonstrate an obvious superiority of our approach over the standard SPM method, and it also outperforms many state-of-the-art methods for several categories.

Paper Details

Date Published: 14 December 2015
PDF: 7 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130U (14 December 2015); doi: 10.1117/12.2203716
Show Author Affiliations
Huabing Zhou, Wuhan Institute of Technology (China)
Yanduo Zhang, Wuhan Institute of Technology (China)
Zhenghong Yu, Guangdong Institute of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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