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2007
IEEE

Video Semantic Concept Discovery using Multimodal-Based Association Classification

12 years 3 months ago
Video Semantic Concept Discovery using Multimodal-Based Association Classification
Digital audio and video have recently taken a center stage in the communication world, which highlights the importance of digital media information management and indexing. It is of great interest for the multimedia research community to find methods and solutions that could help bridge the semantic gap that exists between the low-level features extracted from the audio or video data and the actual semantics of the data. In this paper, we propose a novel framework that works towards reducing this semantic gap. The proposed framework uses the apriori algorithm and association rule mining to find frequent itemsets in the feature data set and generate classification rules to classify video shots to different concepts (semantics). We also introduce a novel pre-filtering architecture which reduces the high positive to negative instances ratio in the classifier training step. This helps reduce the amount of misclassification errors. Our proposed framework shows promising results in cl...
Lin Lin, Guy Ravitz, Mei-Ling Shyu, Shu-Ching Chen
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICMCS
Authors Lin Lin, Guy Ravitz, Mei-Ling Shyu, Shu-Ching Chen
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