A frame-based decision pooling method for video classification

This paper proposes an ingenious and fast method to classify videos into fixed broad classes, which would assist searching and indexing using semantic keywords. The model extracts constituent frames from videos and maps low-level features extracted these frames to high-level semantics. We use color, structure and texture features extracted from a standard image database to train an SVM classifier, to classify videos to five different classes, viz. Mountains, Forests, Buildings, Deserts, and Seas with reasonable accuracy. The model is expected to be quite fast with an optimized implementation as the methods used for feature extraction are not computationally complex and have fast algorithms available.