Contour detection via stacking random forest learning

Chao Zhang, Junchi Yan*, Changsheng Li, Rongfang Bie

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

17 引用 (Scopus)

摘要

Contour detection is an important and fundamental problem in computer vision which finds numerous applications. Despite significant progress has been made in the past decades, contour detection from natural images remains a challenging task due to the difficulty of clearly distinguishing between edges of objects and surrounding backgrounds. To address this problem, we first capture multi-scale features from pixel-level to segment-level using local and global information. These features are mapped to a space where discriminative information is captured by computing posterior divergence of Gaussian mixture models and sufficient statistics based on deep Boltzmann machine. Then we introduce a stacking random forest learning framework for contour detection. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley segmentation and Weizmann horse data sets. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.

源语言英语
页(从-至)2702-2715
页数14
期刊Neurocomputing
275
DOI
出版状态已出版 - 31 1月 2018
已对外发布

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Zhang, C., Yan, J., Li, C., & Bie, R. (2018). Contour detection via stacking random forest learning. Neurocomputing, 275, 2702-2715. https://doi.org/10.1016/j.neucom.2017.11.046