{"title":"A quantitative metric of visual-words separability for a more discriminative visual vocabulary in an unsupervised manner","authors":"Xin Feng, B. Li, Yongxin Ge, Jiaxing Tan","doi":"10.1109/ICICS.2013.6782779","DOIUrl":null,"url":null,"abstract":"The task of visual vocabulary construction plays an important role in the bag-of-words based pattern analysis and robotic applications. A discriminative vocabulary generation in unsupervised case is an open issue for reducing perceptual aliasing in image matching based applications. In this paper, we present a scheme to evaluate the discriminative power of each visual word quantitatively in terms of Mahalanobis separability, and a discriminative visual vocabulary is obtained through adaptively updating the poor discriminative visual words in an unsupervised manner. The effectiveness of our metric is demonstrated in the experiment of loop-closure detection under strong perceptual aliasing condition in both indoor and outdoor image sequences.","PeriodicalId":184544,"journal":{"name":"2013 9th International Conference on Information, Communications & Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 9th International Conference on Information, Communications & Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICS.2013.6782779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The task of visual vocabulary construction plays an important role in the bag-of-words based pattern analysis and robotic applications. A discriminative vocabulary generation in unsupervised case is an open issue for reducing perceptual aliasing in image matching based applications. In this paper, we present a scheme to evaluate the discriminative power of each visual word quantitatively in terms of Mahalanobis separability, and a discriminative visual vocabulary is obtained through adaptively updating the poor discriminative visual words in an unsupervised manner. The effectiveness of our metric is demonstrated in the experiment of loop-closure detection under strong perceptual aliasing condition in both indoor and outdoor image sequences.
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