Video search and indexing with reinforcement agent for interactive multimedia services
Anand Paul, Bowei Chen, Karunanithi Bharanitharan, Jhing-Fa Wang · ACM Transactions on Embedded Computing Systems · 2013
In this study, we present a video search and indexing system based on the state support vector (SVM) network, video graph, and reinforcement agent for recognizing and organizing video events. In order to enhance the recognition performance of the state SVM network, two innovative techniques are presented: state transition correction and transition quality estimation. The classification results are also merged into the video indexing graph, which facilitates the search speed. A reinforcement algorithm with an efficient scheduling scheme significantly reduces both the power consumption and time. The experimental results show the proposed state SVM network was able to achieve a precision rate as high as 83.83% and the query results of the indexing graph reached 80% accuracy. The experiments also demonstrate the performance and feasibility of our system.