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规格化切割符合MRFNormalized Cut meets MRF |
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| 课程网址: | http://videolectures.net/eccv2016_tang_normalized_cut/ |
| 主讲教师: | Meng Tang |
| 开课单位: | 加拿大西安大略大学 |
| 开课时间: | 2016-10-24 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a new segmentation model combining common regularization energies, e.g. Markov Random Field (MRF) potentials, and standard pairwise clustering criteria like Normalized Cut (NC), average association (AA), etc. These clustering and regularization models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show that many common applications using MRF segmentation energies can benefit from a high-order NC term, e.g. enforcing balanced clustering of arbitrary high-dimensional image features combining color, texture, location, depth, motion, etc. On the other hand, standard clustering applications can benefit from an inclusion of common pairwise or higher-order MRF constraints, e.g. edge alignment, bin-consistency, label cost, etc. To address joint energies like NC+MRF, we propose efficient Kernel Cut algorithms based on bound optimization. While focusing on graph cut and move-making techniques, our new unary (linear) kernel and spectral bound formulations for common pairwise clustering criteria allow to integrate them with any regularization functionals with existing discrete or continuous solvers. |
| 关 键 词: | 分割模型; 成对聚类; 边缘对齐 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-07-19：chenxin01 |
| 最后编审: | 2023-07-19：chenxin01 |
| 阅读次数: | 27 |