›› 2017, Vol. 32 ›› Issue (4): 701-713.doi: 10.1007/s11390-017-1752-9

• Special Issue on Deep Learning • Previous Articles     Next Articles

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

Xiang Bai1, Senior Member, IEEE, Zheng Zhang1, Hong-Yang Wang1, Wei Shen2,*   

  1. 1 School of Electronic Information and Communications, Huazhong University of Science and Technology Wuhan 430074, China;
    2 Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
  • Received:2016-12-16 Revised:2017-05-15 Online:2017-07-05 Published:2017-07-05
  • Contact: 10.1007/s11390-017-1752-9 E-mail:wei.shen@t.shu.edu.cn
  • Supported by:

    This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61573160 and 61672336, and in part by the "Chen Guang" Project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation of China under Grant No. 15CG43.

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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