Accelerating Video Feature Extractions in CBVIR on Multicore Systems

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 timeconsuming 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.

[1]  Jing Huang,et al.  Spatial Color Indexing and Applications , 2004, International Journal of Computer Vision.

[2]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[6]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[7]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[8]  Qi Zhang,et al.  Parallelization and Performance Analysis of Video Feature Extractions on Multi-Core Based Systems , 2007, 2007 International Conference on Parallel Processing (ICPP 2007).

[9]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[10]  Atsuo Yoshitaka,et al.  A Survey on Content-Based Retrieval for Multimedia Databases , 1999, IEEE Trans. Knowl. Data Eng..

[11]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Wei-Ying Ma,et al.  Benchmarking of image features for content-based retrieval , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).