Visual event recognition
Gong, Li. · DR-NTU (Nanyang Technological University) · 2013
This report summarizes the work that has been done in the final year project of recognizing visual events in videos. It starts with image recognition, in which im- ages are represented in spatial pyramids. Such representations are then input into SVM and KNN for recognition. In video recognition, bag of words and special- ized Gaussian Mixture Models are employed to represent videos, and respective distance calculation is used to measure video-to-video distance. These distance matrices are then input into SVM for recognition using different kernel types. Also, four domain adaptation methods are implemented to recognize Kodak con- sumer videos using Youtube videos. Adaptive multiple kernel learning achieves the best and improves the mean average precision from 44.33% to 61.40%. Last but not least, a web-based demo system is implemented in two modes to visually demonstrate the underlying recognition system.