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Descripción
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| Content based video indexing and retrieval (CBVIR) is a lively area of research which focuses on automating the indexing, retrieval and management of videos. This area has a wide spectrum of promising applications where assessing the impact of audiovisual productions emerges as a particularly interesting and motivating one. In this paper we present a computational model capable to predict the impact (i.e. positive or negative) upon viewers of car advertisements videos by using a set of visual saliency descriptors. Visual saliency provides information about parts of the image perceived as most important, which are instinctively targeted by humans when looking at a picture or watching a video. For this reason we propose to exploit visual information, introducing it as a new feature which reflects high-level semantics objectively, to improve the video impact categorization results. The suggested salience descriptors are inspired by the mechanisms that underlie the attentional abilities of the human visual system and organized into seven distinct families | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Multimedia Tools And Applications |
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ISSN
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1380-7501 |
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Factor de impacto JCR
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1,53 |
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Información de impacto
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Volumen
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DOI
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10.1007/s11042-017-5339-9 |
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Número de revista
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Desde la página
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1 |
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Hasta la página
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33 |
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Mes
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NOVIEMBRE |
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Ranking
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Journal Rank in Category 48/106; Quartile in Category Q2 |