CASIA OpenIR  > 智能感知与计算研究中心
Window Mining by Clustering Mid-Level Representation for Weakly Supervised Object Localization
Chong Wang; Weiqiang Ren; Kaiqi Huang
2014
Conference NameInternational Conference on Image Processing
Source PublicationProc. International Conference on Image Processing 2014
Pages4067-4071
Conference Date2014-10-01
Conference PlaceParis, France
AbstractDiscovering positive detection windows in training images is a challenging problem in weakly supervised object detection. In this paper, we propose a window mining strategy by the simple and efficient k-means clustering. Firstly, a recent segmentation based object proposal is used for its highly semantic candidate windows; secondly, the bag-of-words model is adopted as mid-level object representation for each window. By clustering these windows with k-means, semantic clusters can be generated. Then, to discover the positive windows from these clusters, we further propose a cluster selection method based on each cluster's discrimination, which is evaluated by classification performance given the category label. With the semantic clusters, this selection process is effective and efficient. Evaluation on the challenging PASCAL VOC 2007 dataset shows that the proposed method outperforms all previous weakly supervised approaches.
KeywordData Mining   image Representation   image Segmentation
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12682
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Chong Wang,Weiqiang Ren,Kaiqi Huang. Window Mining by Clustering Mid-Level Representation for Weakly Supervised Object Localization[C],2014:4067-4071.
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