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Detecting Semantic Concepts from Video Using Temporal Gradients and Audio Classification

Identifieur interne : 001728 ( Main/Exploration ); précédent : 001727; suivant : 001729

Detecting Semantic Concepts from Video Using Temporal Gradients and Audio Classification

Auteurs : Mika Rautiainen [Finlande] ; Tapio Sepp Nen [Finlande] ; Jani Penttil [Finlande] ; Johannes Peltola [Finlande]

Source :

RBID : ISTEX:0B6CD3EE5AB28BFF813BE4D282217527E35EDD21

Abstract

Abstract: In this paper we describe new methods to detect semantic concepts from digital video based on audible and visual content. Temporal Gradient Correlogram captures temporal correlations of gradient edge directions from sampled shot frames. Power-related physical features are extracted from short audio samples in video shots. Video shots containing people, cityscape, landscape, speech or instrumental sound are detected with trained self-organized maps and kNN classification results of audio samples. Test runs and evaluations in TREC 2002 Video Track show consistent performance for Temporal Gradient Correlogram and state-of-the-art precision in audio-based instrumental sound detection.

Url:
DOI: 10.1007/3-540-45113-7_26


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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