José A. Rodríguez-Serrano, F. Perronnin, J. Lladós, Gemma Sánchez
{"title":"A similarity measure between vector sequences with application to handwritten word image retrieval","authors":"José A. Rodríguez-Serrano, F. Perronnin, J. Lladós, Gemma Sánchez","doi":"10.1109/CVPR.2009.5206783","DOIUrl":null,"url":null,"abstract":"This article proposes a novel similarity measure between vector sequences. Recently, a model-based approach was introduced to address this issue. It consists in modeling each sequence with a continuous Hidden Markov Model (CHMM) and computing a probabilistic measure of similarity between C-HMMs. In this paper we propose to model sequences with semi-continuous HMMs (SC-HMMs): the Gaussians of the SC-HMMs are constrained to belong to a shared pool of Gaussians. This constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a probabilistic similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which reduces significantly the computational cost. Experimental results on a handwritten word retrieval task show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses C-HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost (up to 100 times).","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2009.5206783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 35
Abstract
This article proposes a novel similarity measure between vector sequences. Recently, a model-based approach was introduced to address this issue. It consists in modeling each sequence with a continuous Hidden Markov Model (CHMM) and computing a probabilistic measure of similarity between C-HMMs. In this paper we propose to model sequences with semi-continuous HMMs (SC-HMMs): the Gaussians of the SC-HMMs are constrained to belong to a shared pool of Gaussians. This constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a probabilistic similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which reduces significantly the computational cost. Experimental results on a handwritten word retrieval task show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses C-HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost (up to 100 times).