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Automated co-superpixel generation via graph matching

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Abstract

In this paper, a novel ‘co-superpixel’ generation method is proposed via the graph matching. The co-superpixel can capture the common semantic information in coupled images. Therefore, it is significant for various applications in visual pattern recognition. Specifically, we first introduce a superpixel correspondence method based on the graph matching. The main property is that it has the ability to capture the consistent intermediate-level semantic information in coupled images, which can represent the region-based similarity rather than the conventional similarity based on low-level vision features. Second, a new co-superpixel generation method is proposed by the superpixel-merging incorporated with the graph matching cost and the adjacent superpixel appearance similarity in coupled images simultaneously. Furthermore, we extend the proposed co-superpixel method to tackle the object matching problem. The experimental results show that the object matching can be effectively addressed by the co-superpixel. The proposed method is effective for challenging cases in which object appearance changes, deformation and background clutter.

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Acknowledgments

This work was partially supported by NSFC (No.61179060, and 61101091), and Fundamental Research Funds for the Central Universities (ZYGX2012J019).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Yurui Xie, Lingfeng Xu & Zhengning Wang

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  1. Yurui Xie
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  2. Lingfeng Xu
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  3. Zhengning Wang
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Correspondence to Yurui Xie.

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Xie, Y., Xu, L. & Wang, Z. Automated co-superpixel generation via graph matching. SIViP 8, 753–763 (2014). https://doi.org/10.1007/s11760-013-0589-0

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