.png){kind=icon}
.png){kind=icon}
Return
Web video categorization using category-predictive classifiers and category-specific concept classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2016.06.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this era, automatic Web video categorization has become an important multimedia task for organizing and retrieving the plentiful videos on the Web. Due to unbounded variation in both content and quality of Web videos and deficiency in precisely labeled training data, Web video categorization remains a challenging task. In this paper, a novel three-stage framework is proposed for Web video classification using category-predictive classifiers and category-specific concept classifiers, which integrates contextual features and concept-level semantics induced from visual content. First, a content-based category-predictive (CNC) classifier is trained for each category by exploiting visual features to classify Web videos. Second, the significance of concepts for categories is measured with category-specific concept (CSC) classifiers, and it is adopted to refine CNC classifiers at keyframe-level. Third, the context-based category-predictive (CXC) classifiers induced from titles and tags are further combined with the refined CNC classifiers to reinforce the performance. Experiments on two large scale Web video datasets, MCG-WEBV and CCV, demonstrate that the proposed approach achieves promising performance. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Video classification
High-level concept
Web video
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W