article · 2016

Learning · Feature Learning

Shengfeng He, Rynson W. H, Lau Wenxi Liu, Zhe Huang, Qingxiong Yang, Shengfeng He, Rynson W. H, Lau Wenxi, Liu Zhe Huang, Qingxiong Yang

Cited by 0Abstract and details only

Abstract

Abstract Existing computational models for salient object detection primarily rely on hand-crafted fea-tures, which are only able to capture low-level contrast information. In this paper, we learn the hierarchical contrast features by formulating salient object detec-tion as a binary labeling problem using deep learning techniques. A novel superpixelwise convolutional neu-ral network approach, called SuperCNN, is proposed to learn the internal representations of saliency in an effi-cient manner. In contrast to the classical convolutional networks, SuperCNN has four main properties. First, the proposed method is able to learn the hierarchical contrast features, as it is fed by two meaningful super-pixel sequences, which is much more effective for detect-ing salient regions than feeding raw image pixels. Sec-ond, as SuperCNN recovers the contextual information among superpixels, it enables large context to be in-volved in the analysis efficiently. Third, benefiting from the superpixelwise mechanism, the required number of predictions for a densely labeled map is hugely reduced. Fourth, saliency can be detected independent of region size by utilizing a multiscale network structure. Experi-ments show that SuperCNN can robustly detect salient objects and outperforms the state-of-the-art methods on three benchmark datasets.

Cite this paper

APA 7

He, S., H, R. W., Liu, L. W., Huang, Z., Yang, Q., He, S., H, R. W., Wenxi, L., Huang, L. Z., & Yang, Q. (2016). Learning · Feature Learning.

MLA 9

He, Shengfeng, et al. “Learning · Feature Learning.” 2016.

Chicago

He, Shengfeng, Rynson W. H, Lau Wenxi Liu, Zhe Huang, Qingxiong Yang, Shengfeng He, Rynson W. H, Lau Wenxi, Liu Zhe Huang, and Qingxiong Yang. 2016. “Learning · Feature Learning.”

Harvard

He, S. et al. (2016) ‘Learning · Feature Learning’.

BibTeX

@article{he2016learning,
  title = {{Learning · Feature Learning}},
  author = {He, Shengfeng and H, Rynson W. and Liu, Lau Wenxi and Huang, Zhe and Yang, Qingxiong and He, Shengfeng and H, Rynson W. and Wenxi, Lau and Huang, Liu Zhe and Yang, Qingxiong},
  year = {2016}
}

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