Sign language to text by SVM
In this paper is presented an automatic deaf language to text system. The scheme is based on support vector machines (SVM) classifier using a Gaussian kernel. The input parameter vector to SVM is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov mod...
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| creator | Travieso, C.M. Alonso, J.B. Ferrer, M.A. |
| description | In this paper is presented an automatic deaf language to text system. The scheme is based on support vector machines (SVM) classifier using a Gaussian kernel. The input parameter vector to SVM is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov model (HMM). The HMM, which needs a sequence to be trained and used, is fed by the hand contour chain code. Besides, an improvement on the calculation of Fisher score is introduced by means of reducing the kernel scores variance. The error ratio classifying hand letter of the proposed system is less than 0.4% with our database. |
| doi_str_mv | 10.1109/ISSPA.2003.1224907 |
| format | Conference Proceeding |
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The scheme is based on support vector machines (SVM) classifier using a Gaussian kernel. The input parameter vector to SVM is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov model (HMM). The HMM, which needs a sequence to be trained and used, is fed by the hand contour chain code. Besides, an improvement on the calculation of Fisher score is introduced by means of reducing the kernel scores variance. 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The scheme is based on support vector machines (SVM) classifier using a Gaussian kernel. The input parameter vector to SVM is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov model (HMM). The HMM, which needs a sequence to be trained and used, is fed by the hand contour chain code. Besides, an improvement on the calculation of Fisher score is introduced by means of reducing the kernel scores variance. The error ratio classifying hand letter of the proposed system is less than 0.4% with our database.</description><subject>Color</subject><subject>Computer errors</subject><subject>Deafness</subject><subject>Handicapped aids</subject><subject>Hidden Markov models</subject><subject>Image databases</subject><subject>Kernel</subject><subject>Partial response channels</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><isbn>0780379462</isbn><isbn>9780780379466</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KAzEURgMiqG1fQF3kBWa8Nzc_k2Up_hRaWhjbbckkN8NIrdIZwb69BfttzuZw4BPiHqFEBP80r-v1tFQAVKJS2oO7EnfgKiDntVU3YtL3H3CeNuStuxWPddce5D4c2p_Qshy-5MC_g2xOst4ux-I6h33PkwtHYvPy_D57Kxar1_lsuig6dGYoiKM2Nmm0ERvW1hICBWtTYMoeo6pyjpFN8jYQ-szOuLPWpGSSC6qikXj473bMvPs-dp_heNpdDtAfxak6SQ</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Travieso, C.M.</creator><creator>Alonso, J.B.</creator><creator>Ferrer, M.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2003</creationdate><title>Sign language to text by SVM</title><author>Travieso, C.M. ; Alonso, J.B. ; Ferrer, M.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-3ec456d416c1be4663103a66dae3f91c28ffcce5d96a319fe757e46bdd5d7a283</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Color</topic><topic>Computer errors</topic><topic>Deafness</topic><topic>Handicapped aids</topic><topic>Hidden Markov models</topic><topic>Image databases</topic><topic>Kernel</topic><topic>Partial response channels</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Travieso, C.M.</creatorcontrib><creatorcontrib>Alonso, J.B.</creatorcontrib><creatorcontrib>Ferrer, M.A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Travieso, C.M.</au><au>Alonso, J.B.</au><au>Ferrer, M.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sign language to text by SVM</atitle><btitle>Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings</btitle><stitle>ISSPA</stitle><date>2003</date><risdate>2003</risdate><volume>2</volume><spage>435</spage><epage>438 vol.2</epage><pages>435-438 vol.2</pages><isbn>0780379462</isbn><isbn>9780780379466</isbn><abstract>In this paper is presented an automatic deaf language to text system. The scheme is based on support vector machines (SVM) classifier using a Gaussian kernel. The input parameter vector to SVM is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov model (HMM). The HMM, which needs a sequence to be trained and used, is fed by the hand contour chain code. Besides, an improvement on the calculation of Fisher score is introduced by means of reducing the kernel scores variance. The error ratio classifying hand letter of the proposed system is less than 0.4% with our database.</abstract><pub>IEEE</pub><doi>10.1109/ISSPA.2003.1224907</doi></addata></record> |
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| ispartof | Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings, 2003, Vol.2, p.435-438 vol.2 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Color Computer errors Deafness Handicapped aids Hidden Markov models Image databases Kernel Partial response channels Support vector machine classification Support vector machines |
| title | Sign language to text by SVM |
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