Web-based Beowulf-Class parallel computing on image database indexing and retrieval system

Content-based image retrieval (CBIR) techniques will be part of the next generation of search engines. However, even with a suitable retrieval method, real-time image retrieval from a large image database still requires extremely high computational power. In this paper, a Web-based image database indexing and retrieval system is described. It uses fast CBIR with Beowulf-Class parallel computing engine (Super Abacus) to efficiently and effectively shorten the retrieval time. Content-based image retrieval with several parallel algorithms for images which query different color spaces and the compressed-domain of the images are developed to achieve higher efficiency. Text-based query is also supported for images using a conventional annotation approach.

[1]  Ling Guan,et al.  Image retrieval based on energy histograms of the low frequency DCT coefficients , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[2]  Roberto Brunelli,et al.  On the use of histograms for image retrieval , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[3]  Michael J. Swain,et al.  The Capacity and the Sensitivity of Color Histogram Indexing , 1994 .

[4]  M. Wand.  Data-Based Choice of Histogram Bin Width , 1997 .

[5]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[6]  Vijay V. Raghavan,et al.  Content-Based Image Retrieval Systems - Guest Editors' Introduction , 1995, Computer.