| Title | Structure-Sensitive Superpixels via Geodesic Distance |
| Authors | Wang, Peng Zeng, Gang Gan, Rui Wang, Jingdong Zha, Hongbin |
| Affiliation | Peking Univ, Key Lab Machine Percept, Beijing 100871, Peoples R China. Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China. Microsoft Res Asia, Beijing, Peoples R China. |
| Keywords | Superpixel segmentation Geodesic distance Iterative optimization Structure-sensitivity IMAGE SEGMENTATION K-MEANS ALGORITHM RECOGNITION CONTEXT CUES |
| Issue Date | 2013 |
| Publisher | 国际计算机视觉杂志 |
| Citation | INTERNATIONAL JOURNAL OF COMPUTER VISION.2013,103,(1),1-21. |
| Abstract | Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd's algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach. |
| URI | http://hdl.handle.net/20.500.11897/391278 |
| ISSN | 0920-5691 |
| DOI | 10.1007/s11263-012-0588-6 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 机器感知与智能教育部重点实验室 数学科学学院 |
