Semantic Video Concept Detection using Novel Mixed-Hybrid-Fusion Approach for Multi-Label Data
Janwe, Nitin Jagannathrao
Bhoyar, Kishor (Yeshwantrao Chavan College of Engineering (Nagpur, Índia))

Fecha:	2017
Resumen:	The performance of the semantic concept detection method depends on, the selection of the low-level visual features used to represent key-frames of a shot and the selection of the feature-fusion method used. This paper proposes a set of low-level visual features of considerably smaller size and also proposes novel 'hybrid-fusion' and 'mixed-hybrid-fusion', approaches which are formulated by combining early and late-fusion strategies proposed in the literature. In the initially proposed hybrid-fusion approach, the features from the same feature group are combined using early-fusion before classifier training; and the concept probability scores from multiple classifiers are merged using late-fusion approach to get final detection scores. A feature group is defined as the features from the same feature family such as color moment. The hybrid-fusion approach is refined and the "mixed-hybrid-fusion" approach is proposed to further improve detection rate. This paper presents a novel video concept detection system for multi-label data using a proposed mixed-hybrid-fusion approach. Support Vector Machine (SVM) is used to build classifiers that produce concept probabilities for a test frame. The proposed approaches are evaluated on multi-label TRECVID2007 development dataset. Experimental results show that, the proposed mixed-hybrid-fusion approach performs better than other proposed hybrid-fusion approach and outperforms all conventional early-fusion and late-fusion approaches by large margins with respect to feature set dimensionality and Mean Average Precision (MAP) values.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Semantic video concept detection ; High-level feature extraction ; Semantic gap ; Video retrieval ; Support vector machine ; Hybrid-fusion ; Mixed-hybrid-fusion ; Multi-label classification
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 3 (2017) , p. 14-29 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v16-n3-janwe
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/329291
DOI: 10.5565/rev/elcvia.927

16 p, 2.3 MB

El registro aparece en las colecciones:
Artículos > Artículos publicados > ELCVIA
Artículos > Artículos de investigación