Content-Based Video Information Retrieval (CBVIR) has becoming one of the best solutions for retrieving useful information from today?s video information explosion. And with the rapid development of modern technologies, CBVIR is emerging as a mass market desktop application. There is evidence that visual feature extraction is the most time-consuming part in a CBVIR system. In this paper, we implement three video visual feature extractions in parallel by exploring different kinds of thread-level parallelism. We also conduct detailed scalability and memory performance analysis on two multi-core based systems, in order to gain more insights into video-analysis related applications on future multi-core systems. From our analysis we identify the likely causes of bottlenecks in these kinds of applications and suggest ways to improve scalability.