图 书 馆
用于高效物体检测的固定特征和折叠层次结构Stationary Features and Folded Hierarchies for Efficient Object Detection |
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| 课程网址: | http://videolectures.net/eml07_geman_sff/ |
| 主讲教师: | Donald Geman |
| 开课单位: | 约翰霍普金斯大学 |
| 开课时间: | 2007-12-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Most discriminative techniques for detecting instances from object categories in still images consist of looping over a partition of a pose space with dedicated binary classifiers. This strategy is inefficient for a complex pose, i.e., for fine-grained descriptions: i) fragmenting the training data, which is inevitable in dealing with high in-class variation, severely reduces accuracy; ii) the computational cost at high pose resolution is prohibitive due to visiting a massive pose partition. \\ To overcome data-fragmentation I will discuss a novel framework centered on pose-indexed, stationary features, which allows for efficient, one-shot learning of pose-specific classifiers. Such features assign a response to a pair consisting of an image and a pose, and are designed so that the probability distribution of the response is constant if an object is actually present. To avoid expensive scene processing, the classifiers are arranged in a hierarchy based on nested partitions of the pose, which allows for efficient search. The hierarchy is then "folded" for training: all the classifiers at each level are derived from one base predictor learned from all the data. The hierarchy is "unfolded" for testing: parsing a scene amounts to examining increasingly finer object descriptions only when there is sufficient evidence for coarser ones. I will illustrate these ideas by detecting and localizing cats in highly cluttered greyscale scenes. This is joint work with Francois Fleuret. |
| 关 键 词: | 静止图像; 辨别技术; 二进制分类器 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-10：lxf |
| 阅读次数: | 47 |