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Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation

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Abstract

Edges are important cues for localizing object proposals. The recent progresses to this problem are mostly driven by defining effective objectness measures based on edge cues. In this paper, we develop a new representation named directional edges on which each edge pixel is assigned with a direction toward object center, through learning a direction prediction model with convolutional neural networks in a holistic manner. Based on directional edges, two new objectness measures are designed for ranking object proposals. Experiments show that the proposed method achieves 97.1% object recall at an overlap threshold of 0.5 and 81.9% object recall at an overlap threshold of 0.7 at 1 000 proposals on the PASCAL VOC 2007 test dataset, which is superior to the state-of-the-art methods.

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Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China

    Xiang Bai, Zheng Zhang & Hong-Yang Wang

  2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, 200444, China

    Wei Shen

Authors
  1. Xiang Bai
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  2. Zheng Zhang
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  3. Hong-Yang Wang
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  4. Wei Shen
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Correspondence to Wei Shen.

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Bai, X., Zhang, Z., Wang, HY. et al. Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation. J. Comput. Sci. Technol. 32, 701–713 (2017). https://doi.org/10.1007/s11390-017-1752-9

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  • DOI: https://doi.org/10.1007/s11390-017-1752-9

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