An intelligent video categorization engine

Abstract

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.

Citation

Hong, G.Y., Fong, B. and Fong, A.C.M. (2005), "An intelligent video categorization engine", Kybernetes, Vol. 34 No. 6, pp. 784-802. https://doi.org/10.1108/03684920510595490