Event recognition is a crucial task to provide high-level semantic description of the video content. The bag-of-words (BoW) approach has proven to be successful for the categorization of objects and scenes in images, but it is unable to model temporal information between consecutive frames. In this paper we present a method to introduce temporal information for video event recognition within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW. The sequences are treated as strings (phrases) where each histogram is considered as a character. Event classification of these sequences of variable length, depending on the duration of the video clips, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two domains, soccer videos and a subset of TRECVID 2005 news videos, demonstrate the validity of the proposed approach.

Video Event Classification using String Kernels / Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - STAMPA. - 48:1(2010), pp. 69-87. [10.1007/s11042-009-0351-3]

Video Event Classification using String Kernels

SERRA, GIUSEPPE
2010

Abstract

Event recognition is a crucial task to provide high-level semantic description of the video content. The bag-of-words (BoW) approach has proven to be successful for the categorization of objects and scenes in images, but it is unable to model temporal information between consecutive frames. In this paper we present a method to introduce temporal information for video event recognition within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW. The sequences are treated as strings (phrases) where each histogram is considered as a character. Event classification of these sequences of variable length, depending on the duration of the video clips, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two domains, soccer videos and a subset of TRECVID 2005 news videos, demonstrate the validity of the proposed approach.
48:1
69
87
Video Event Classification using String Kernels / Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - STAMPA. - 48:1(2010), pp. 69-87. [10.1007/s11042-009-0351-3]
Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/979941
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 41
  • ???jsp.display-item.citation.isi??? 24
social impact