Please use this identifier to cite or link to this item:
|Title:||A comprehensive representation scheme for video semantic ontology and its applications in semantic concept detection|
|Authors:||Zha, Z.-J. |
Video concept detection
|Citation:||Zha, Z.-J., Mei, T., Zheng, Y.-T., Wang, Z., Hua, X.-S. (2012). A comprehensive representation scheme for video semantic ontology and its applications in semantic concept detection. Neurocomputing 95 : 29-39. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2011.05.044|
|Abstract:||Recent research has discovered that leveraging ontology is an effective way to facilitate semantic video concept detection. As an explicit knowledge representation, a formal ontology definition usually consists of a lexicon, properties, and relations. In this paper, we present a comprehensive representation scheme for video semantic ontology in which all the three components are well studied. Specifically, we leverage LSCOM to construct the concept lexicon, describe concept property as the weights of different modalities which are obtained manually or by data-driven approach, and model two types of concept relations (i.e., pairwise correlation and hierarchical relation). In contrast with most existing ontologies which are only focused on one or two components for domain-specific videos, the proposed ontology is more comprehensive and general. To validate the effectiveness of this ontology, we further apply it to video concept detection. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared to existing key approaches to video concept detection. © 2012 Elsevier B.V..|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 18, 2018
WEB OF SCIENCETM
checked on Mar 27, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.