An intelligent video categorization engine
Guan Yue Hong, Brendan Fong, Alvis C. M. Fong · Kybernetes · 2005
Purpose: – We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories). Design/methodology/approach: – We provide a survey of existing techniques that have been proposed, either directly or indirectly, towards achieving intelligent video categorization. We also compare the performance of two popular ANNs: Kohonen's self‐organizing map (SOM) and fuzzy adaptive resonance theory (Fuzzy ART). In particular, the ANNs are trained offline to form the necessary knowledge base prior to online categorization. Findings: – Experimental results show that accurate categorization can be achieved near instantaneously. Research limitations: – The main limitation of this research is the need for a finite set of predefined categories. Further research should focus on generalization of such techniques. Originality/value: – Machine understanding of video footage has tremendous potential for three reasons. First, it enables interactive broadcast of video. Second, it allows unequal error protection for different video shots/segments during transmission to make better use of limited channel resources. Third, it provides intuitive indexing and retrieval for video‐on‐demand applications.