图 书 馆
图像分析的加权子结构挖掘Weighted Substructure Mining for Image Analysis |
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| 课程网址: | http://videolectures.net/mlg07_nowozin_wsm/ |
| 主讲教师: | Sebastian Nowozin |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2007-09-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and the linear support vector machine, one can partly fulfill the goal, but the accuracy of linear classifiers is not high and the obtained features are not informative for users. We propose to combine item set mining and large margin classifiers to select features from the power set of all visual words. Our resulting classification rule is easier to browse and simpler to understand, because each feature has richer information. As a next step, each image is represented as a graph where nodes correspond to local image features and edges encode geometric relations between features. Combining graph mining and boosting, we can obtain a classification rule based on subgraph features that contain more information than the set features. We evaluate our algorithm in a web-retrieval ranking task where the goal is to reject outliers from a set of images returned for a keyword query. Furthermore, it is evaluated on the supervised classification tasks with the challenging VOC2005 data set. Our approach yields excellent accuracy in the unsupervised ranking task and competitive results in the supervised classification task. |
| 关 键 词: | 图像分类; 线性支持向量机; 异常值 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-06-30：cjy |
| 阅读次数: | 14 |