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Error-tolerant sign retrieval using visual features and maximum a posteriori estimation.

Chung-Hsien Wu·Yu-Hsien Chiu·Kung-Wei Cheng
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Abstract

This paper proposes an efficient error-tolerant approach to retrieving sign words from a Taiwanese Sign Language (TSL) database. This database is tagged with visual gesture features and organized as a multilist code tree. These features are defined in terms of the visual characteristics of sign gestures by which they are indexed for sign retrieval and displayed using an anthropomorphic interface. The maximum a posteriori estimation is exploited to retrieve the most likely sign word given the input feature sequence. An error-tolerant mechanism based on mutual information criterion is proposed to retrieve a sign word of interest efficiently and robustly. A user-friendly anthropomorphic interface is also developed to assist learning TSL. Several experiments were performed in an educational environment to investigate the system's retrieval accuracy. Our proposed approach outperformed a dynamic programming algorithm in its task and shows tolerance to user input errors.

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AlgorithmsArmArtificial IntelligenceCluster AnalysisCommunication Devices for People with DisabilitiesComputer SimulationHumansImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalNatural Language ProcessingNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedQuality ControlReproducibility of ResultsSensitivity and SpecificitySign LanguageSignal Processing, Computer-AssistedSubtraction TechniqueUser-Computer InterfaceComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Chung-Hsien Wu, Yu-Hsien Chiu, Kung-Wei Cheng. 2004. Error-tolerant sign retrieval using visual features and maximum a posteriori estimation.. https://doi.org/10.1109/tpami.2004.1265864

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