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dc.contributor.authorAgustí Ballester, Pau
dc.contributor.authorTraver Roig, Vicente Javier
dc.contributor.authorPla, Filiberto
dc.description.abstractThe bag-of-words (BoW) representation has successfully been used for human action recognition from videos. However, one limitation of the standard BoW is that it ignores spatial and temporal relationships between the visual words. Although several approaches have been proposed to deal with this issue, we propose an extension which is arguably simpler yet quite effective. The proposed representation, t-BoW, captures only temporal relationships between pairs of words in an aggregated way by counting co-occurrences at several temporal differences. Unlike other approaches, neither spatial nor hierarchical information is accounted for explicitly, and no significant change is required in the quantization or classification procedures. Performance improvements over the traditional BoW and other BoW extensions are experimentally observed in the KTH, the ADL, the Keck, and the HMDB51 action/gestures datasets.ca_CA
dc.format.extent7 p.ca_CA
dc.relation.isPartOfPattern Recognition Letters Volume 49, 1 November 2014, Pages 224–230ca_CA
dc.rights© 2014 Elsevier B.V. All rights reserved.ca_CA
dc.subjectHuman action recognitionca_CA
dc.subjectTemporal constraintsca_CA
dc.titleBag-of-words with aggregated temporal pair-wise word co-occurrence for human action recognitionca_CA

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