FastLCD: Fast Label Coordinate Descent for the Efficient Optimization of 2D Label MRFs | |
Liu KW(刘康伟)![]() ![]() ![]() ![]() | |
2016 | |
会议日期 | 2016 |
会议地点 | 美国 |
关键词 | 马尔科夫随机场 标号坐标梯度下降 |
英文摘要 |
Recently, MRFs with two-dimensional (2D) labels have proved useful to many applications, such as image matching and optical flow estimation. Due to the huge 2D label set in these problems, existing optimization algorithms tend to be slow for the
inference of 2D label MRFs, and this greatly limits the practical use of 2D label MRFs. To solve the problem, this paper presents an efficient algorithm, named FastLCD. Unlike previous popular movemaking algorithms (e.g., α-expansion) that visit all the labels exhaustively in each step, FastLCD optimizes the 2D label MRFs by performing label
coordinate descents alternately in horizontal, vertical and diagonal directions, and by this way, it does not need to visit all the labels exhaustively. FastLCD greatly reduces the search space of the label set and benefits from a lower time complexity. Experimental results show that FastLCD is much faster, while it still yields high quality results. |
会议录 | International Joint Conference on Artificial Intelligence
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语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/11830] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Huang KQ(黄凯奇) |
作者单位 | 中科院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu KW,Zhang JG,Yang PP,et al. FastLCD: Fast Label Coordinate Descent for the Efficient Optimization of 2D Label MRFs[C]. 见:. 美国. 2016. |
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