UNSUPERVISED FOREGROUND DETECTION IN UNLABELED IMAGES
This paper presents an unsupervised method to simultaneously identify foreground objects and improve image clustering quality. Given a set of unlabeled images, each image is decomposed into a set of local features and feature weights. First, the unlabeled images are automatically clustered based on feature appearance similarity and geometry similarity. Then, feature weights are recomputed according to appearance and geometry correspondences within the clustering results. Finally, we iteratively refines the clustering results as well as feature weights which reflect the degree of features belong to foreground objects. These novel aspects lead to precise foreground feature identification and improvement in overall detection performance compared with previous methods in Caltech-101 dataset
foreground detection unsupervised learning object recognition
XIE XIAOMENG LIANG PENG QIN JIANGWEI
Department of Computer Science and Engineering South China University of Technology Guangdong 510006,China
国际会议
上海
英文
405-408
2011-03-11(万方平台首次上网日期,不代表论文的发表时间)