Multi-Core Software

Accelerating Video Feature Extractions in CBVIR on Multi-Core Systems

Yurong Chen, Corporation Technology Group, Intel Corporation
Eric Li, Corporation Technology Group, Intel Corporation
Jianguo Li, Corporation Technology Group, Intel Corporation
Yimin Zhang, Corporation Technology Group, Intel Corporation

Index words: contend-based video information retrieval, multi-core, optimization, parallel computing, performance analysis

Citations for this paper. Chen, Y.; Li, E.; Li, J.; Zhang, Y. "Accelerating Video Feature Extractions in CBVIR on Multi-Core Systems." Intel Technology Journal. http://www.intel.com/technology/itj/2007/
v11i4/8-video/1-abstract.htm (November 2007).

ABSTRACT

With the explosive increase in video data, automatic video management (search/retrieval) is becoming a mass market application, and Content-Based Video Information Retrieval (CBVIR) is one of the best solutions. Most CBVIR systems are based on low-level feature extractions guided by the MPEG-7 standard for high-level semantic concept indexing. It is well known that CBVIR is a very compute-intensive task, and the low-level visual feature extractions are the most time-consuming components in CBVIR. Nowadays, with the multi-core processor becoming mainstream, CBVIR can be accelerated by fully utilizing the computing power of available multi-core processors.

In this paper, we optimize and parallelize a set of typical visual feature extraction applications in CBVIR. The underlying optimization and parallel techniques are representative of those used in video-analysis applications and can be further used in other applications to maximally improve their performance on multi-core systems. We conduct a detailed performance analysis of these parallel applications on a dual-socket, quad-core system. The analysis helps us identify possible causes of bottlenecks, and we suggest avenues for scalability improvement to make those applications more powerful in real-time performance.

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