Video annotation using softcomputing di potnurwar archana (5 risultati)

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  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

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    Da: moluna, Greven, Germaniamoluna

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    EUR 45,45

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    Kartoniert / Broschiert. Condizione: New.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

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    Da: preigu, Osnabrück, Germaniapreigu

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    EUR 47,95

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    Taschenbuch. Condizione: Neu. Video Annotation Using Softcomputing | Archana Potnurwar (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204199887 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Aug 2021, 2021

    6204199889 / 9786204199887

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    • Print on Demand

    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 54,90

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Domain adaptive video annotation has received significant attention, due to the large increase in digital data. Video Annotation encounters many difficulties, such as insufficiency of training data, curse of dimensionality and the problem of semantic gap. As the manual annotation takes more time and is labor intensive, therefore automatic annotation is highly desirable. In this book, we have proposed a framework for effective video annotation [AVA-VC] which is based on visual content. The Sailency feature extraction technique is used initially, which is followed by shot detection and two level keyframe extraction technique. The proposed feature extraction technique and use of COREL5 image database improves the result of of video annotation. Generation of the weight vector in the training phase and using this newly generated weight vector to find out the annotation, leads in improving the performance. Trecvid dataset is used to test the performance of the proposed algorithm. The proposed AVA-VC outperforms for 38 and 36 concepts on MAP, when it is compared with well known algorithms OMG- SSL and MMT-MGO. 128 pp. Englisch.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

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    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    EUR 79,05

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    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Domain adaptive video annotation has received significant attention, due to the large increase in digital data. Video Annotation encounters many difficulties, such as insufficiency of training data, curse of dimensionality and the problem of semantic gap. As the manual annotation takes more time and is labor intensive, therefore automatic annotation is highly desirable. In this book, we have proposed a framework for effective video annotation [AVA-VC] which is based on visual content. The Sailency feature extraction technique is used initially, which is followed by shot detection and two level keyframe extraction technique. The proposed feature extraction technique and use of COREL5 image database improves the result of of video annotation. Generation of the weight vector in the training phase and using this newly generated weight vector to find out the annotation, leads in improving the performance. Trecvid dataset is used to test the performance of the proposed algorithm. The proposed AVA-VC outperforms for 38 and 36 concepts on MAP, when it is compared with well known algorithms OMG- SSL and MMT-MGO.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Aug 2021, 2021

    6204199889 / 9786204199887

    • Brossura
    • Print on Demand

    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Condizione: Nuovo

    EUR 54,90

    EUR 60,00 spedizione 
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    Quantità: 1 disponibili

    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Domain adaptive video annotation has received significant attention, due to the large increase in digital data. Video Annotation encounters many difficulties, such as insufficiency of training data, curse of dimensionality and the problem of semantic gap. As the manual annotation takes more time and is labor intensive, therefore automatic annotation is highly desirable. In this book, we have proposed a framework for effective video annotation [AVA-VC] which is based on visual content. The Sailency feature extraction technique is used initially, which is followed by shot detection and two level keyframe extraction technique. The proposed feature extraction technique and use of COREL5 image database improves the result of of video annotation. Generation of the weight vector in the training phase and using this newly generated weight vector to find out the annotation, leads in improving the performance. Trecvid dataset is used to test the performance of the proposed algorithm. The proposed AVA-VC outperforms for 38 and 36 concepts on MAP, when it is compared with well known algorithms OMG- SSL and MMT-MGO.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch.