Articoli correlati a Video Annotation Using Softcomputing
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Video Annotation Using Softcomputing
Immagini fornite dal venditore
Video Annotation Using Softcomputing
Editore:
LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204199889
ISBN 13: 9786204199887
Nuovo
Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
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. Codice articolo 9786204199887
Compra nuovo
EUR 54,90
Spedizione EUR 23,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 2 disponibili
Immagini fornite dal venditore
Video Annotation Using Softcomputing
Editore:
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199889
ISBN 13: 9786204199887
Nuovo
Kartoniert / Broschiert
Da: moluna, Greven, Germania
Valutazione del venditore 5 su 5 stelle
Kartoniert / Broschiert. Condizione: New. Codice articolo 509885209
Compra nuovo
EUR 45,45
Spedizione EUR 48,99
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Video Annotation Using Softcomputing
Editore:
LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204199889
ISBN 13: 9786204199887
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
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. Codice articolo 9786204199887
Compra nuovo
EUR 54,90
Spedizione EUR 60,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 1 disponibili
Immagini fornite dal venditore
Video Annotation Using Softcomputing
Editore:
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199889
ISBN 13: 9786204199887
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
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. Codice articolo 9786204199887
Compra nuovo
EUR 55,56
Spedizione EUR 61,05
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 1 disponibili
Immagini fornite dal venditore
Video Annotation Using Softcomputing
Editore:
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199889
ISBN 13: 9786204199887
Nuovo
Taschenbuch
Da: preigu, Osnabrück, Germania
Valutazione del venditore 5 su 5 stelle
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. Codice articolo 120567696
Compra nuovo
EUR 47,90
Spedizione EUR 70,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 5 disponibili