Video annotation using softcomputing de potnurwar archana (5 resultados)

Autor
Título
Refinar con la Búsqueda avanzada

Filtrar la búsqueda

  • Libros (5)

  • Nuevo (5)

a

Intervalo de precios personalizado (EUR)

a

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

    • Tapa blanda

    Librería: moluna, Greven, Alemaniamoluna

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 45,45

    Envío por EUR 48,99 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: Más de 20 disponibles

    Kartoniert / Broschiert. Condición: New.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

    • Tapa blanda

    Librería: preigu, Osnabrück, Alemaniapreigu

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 47,95

    Envío por EUR 70,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 5 disponibles

    Taschenbuch. Condición: 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.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing Aug 2021, 2021

    6204199889 / 9786204199887

    • Tapa blanda
    • Impresión bajo demanda

    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 54,90

    Envío por EUR 23,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Taschenbuch. Condición: 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.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2021

    6204199889 / 9786204199887

    • Tapa blanda
    • Impresión bajo demanda

    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 79,05

    Envío por EUR 35,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Taschenbuch. Condición: 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.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing Aug 2021, 2021

    6204199889 / 9786204199887

    • Tapa blanda
    • Impresión bajo demanda

    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 54,90

    Envío por EUR 60,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Taschenbuch. Condición: 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.