图 书 馆
大型物体识别系统Large-Scale Object Recognition Systems |
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| 课程网址: | http://videolectures.net/etvc08_schmid_lsors/ |
| 主讲教师: | Cordelia Schmid |
| 开课单位: | 法国国家信息与自动化研究所 |
| 开课时间: | 2008-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper introduces recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We first analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on * Hamming embedding (HE) and * weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within the inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy. |
| 关 键 词: | 图像搜索; 特征图像; 特征包 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-13：cwx |
| 阅读次数: | 71 |