Improving bag-of-words scheme for scene categorization

摘要：

　　Bag-of-words (BoW) representation becomes one of the most popular methods for representing image content and has been successfully applied to object categorization.This paper uses the newly proposed statistics of word activation forces (WAFs) to reduce the redundancy in the codebook used in the BoW model.In such a way,the representation of image features is improved.In addition,the authors propose a method using soft inverse document frequency (Sofl-IDF) to optimize BoW based image features.Given visual words and the dataset,each visual word appears in different amount of images and also different times in each particular image.Some of the visual words appear rare in contrary to the frequent ones.The proposed method balances this case.Experiments show encouraging results in scene categorization by the proposed approach.

关键词： bag-of-words word activation forces soft inverse document frequency scene categorization

作者: LI Qun ZHANG Hong-gang GUO Jun BHANU Bir AN Le

作者单位: School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio Center for Research in Intelligent Systems,University of California,Riverside,CA 92521,USA

会议类型: 国内会议

会议名称: 第六届中国传感器网络学术会议(CWSN 2012)

会议地点: 黄山

会议语种:英文

页码: 166-171

在线出版日期: 2012-10-25（万方平台首次上网日期，不代表论文的发表时间）