Inhaltsangabe
Video event detection is one of the most important research topics in computer science. The recognition of complex events, e.g. “birthday party”, “wedding ceremony” or “attempting a bike trick”, is even more difficult since complex events consist of various human interactions with different objects in diverse environments with variable time intervals. In this book, we propose two learning methods. The first proposed method, named maximal evidence learning (MEL), is based on a large-margin formulation that treats instance labels as hidden latent variables, and infers the instance labels and the instance-level classification model simultaneously. MEL can infer optimal solutions by learning as many positive instances as possible from positive videos, and negative instances from negative videos. The second proposed method is called evidence selective ranking (ESR). ESR is based on static-dynamic instance embedding, and employs infinite push ranking to select the most distinctive evidence. Extensive analysis on large-scale video event datasets shows significant performance gains by both methods.In addition, we also demonstrate key selected evidence is meaningful to humans and can be used to locate video segments that signify an event.
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Über die Autorin bzw. den Autor
Dr. Kuan-Ting Lai received his Ph.D. degree (2009-2014) from National Taiwan University. During 2012-13, Dr. Lai was a visiting scholar at Columbia University and co-worked with IBM T.J. Watson Research Center. His researches include computer vision, machine learning and Internet of Things (IoT). He is currently the VP of technology at Arkados Group.
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Learning Key Evidence for Detecting Complex Events in Videos
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Video event detection is one of the most important research topics in computer science. The recognition of complex events, e.g. ¿birthday party¿, ¿wedding ceremony¿ or ¿attempting a bike trick¿, is even more difficult since complex events consist of various human interactions with different objects in diverse environments with variable time intervals. In this book, we propose two learning methods. The first proposed method, named maximal evidence learning (MEL), is based on a large-margin formulation that treats instance labels as hidden latent variables, and infers the instance labels and the instance-level classification model simultaneously. MEL can infer optimal solutions by learning as many positive instances as possible from positive videos, and negative instances from negative videos. The second proposed method is called evidence selective ranking (ESR). ESR is based on static-dynamic instance embedding, and employs infinite push ranking to select the most distinctive evidence. Extensive analysis on large-scale video event datasets shows significant performance gains by both methods.In addition, we also demonstrate key selected evidence is meaningful to humans and can be used to locate video segments that signify an event. 88 pp. Englisch. Bestandsnummer des Verkäufers 9783639819755
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Learning Key Evidence for Detecting Complex Events in Videos
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorrnrnDr. Kuan-Ting Lai received his Ph.D. degree (2009-2014) from National Taiwan University. During 2012-13, Dr. Lai was a visiting scholar at Columbia University and co-worked with IBM T.J. Watson Research Center. His researc. Bestandsnummer des Verkäufers 507971490
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Taschenbuch. Zustand: Neu. Neuware -Video event detection is one of the most important research topics in computer science. The recognition of complex events, e.g. ¿birthday party¿, ¿wedding ceremony¿ or ¿attempting a bike trick¿, is even more difficult since complex events consist of various human interactions with different objects in diverse environments with variable time intervals. In this book, we propose two learning methods. The first proposed method, named maximal evidence learning (MEL), is based on a large-margin formulation that treats instance labels as hidden latent variables, and infers the instance labels and the instance-level classification model simultaneously. MEL can infer optimal solutions by learning as many positive instances as possible from positive videos, and negative instances from negative videos. The second proposed method is called evidence selective ranking (ESR). ESR is based on static-dynamic instance embedding, and employs infinite push ranking to select the most distinctive evidence. Extensive analysis on large-scale video event datasets shows significant performance gains by both methods.In addition, we also demonstrate key selected evidence is meaningful to humans and can be used to locate video segments that signify an event.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. Bestandsnummer des Verkäufers 9783639819755
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Video event detection is one of the most important research topics in computer science. The recognition of complex events, e.g. ¿birthday party¿, ¿wedding ceremony¿ or ¿attempting a bike trick¿, is even more difficult since complex events consist of various human interactions with different objects in diverse environments with variable time intervals. In this book, we propose two learning methods. The first proposed method, named maximal evidence learning (MEL), is based on a large-margin formulation that treats instance labels as hidden latent variables, and infers the instance labels and the instance-level classification model simultaneously. MEL can infer optimal solutions by learning as many positive instances as possible from positive videos, and negative instances from negative videos. The second proposed method is called evidence selective ranking (ESR). ESR is based on static-dynamic instance embedding, and employs infinite push ranking to select the most distinctive evidence. Extensive analysis on large-scale video event datasets shows significant performance gains by both methods.In addition, we also demonstrate key selected evidence is meaningful to humans and can be used to locate video segments that signify an event. Bestandsnummer des Verkäufers 9783639819755
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Taschenbuch. Zustand: Neu. Learning Key Evidence for Detecting Complex Events in Videos | Kuan-Ting Lai | Taschenbuch | Englisch | 2015 | ¿¿¿¿¿¿¿ | EAN 9783639819755 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 120541321
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