Image classification based on region of interest detection

Huabing Zhou; Yanduo Zhang; Zhenghong Yu

doi:10.1117/12.2203716

14 December 2015 Image classification based on region of interest detection

Huabing Zhou, Yanduo Zhang, Zhenghong Yu

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130U (2015) https://doi.org/10.1117/12.2203716
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Huabing Zhou, Yanduo Zhang, and Zhenghong Yu "Image classification based on region of interest detection", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130U (14 December 2015); https://doi.org/10.1117/12.2203716

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