In huge databases, Image processing takes more time for execution on a single core processor because of slow single thread algorithms. Graphics Processing Unit (GPU) is more popular now-a-days due to their speed, programmability, low cost and more inbuilt execution cores in it. Most of the researchers started work to use GPUs as a processing unit with a single core computer system to speedup execution of algorithms. The main goal of this research work is to parallelize the process of content based image retrieval through texture and that to in compressed domain making whole process much faster than normal. In this paper, parallel implementation is focused on the well known Euclidean Distance approach for texture based image retrieval systems, since it is one of the most fundamental and important problems in the field of computer vision and content based image retrieval (CBIR) and for compressed images we have taken standard JPEG format. Our work employs extensive usage of highly multithreaded architecture of multi-cored GPU. An efficient use of shared memory is required to optimize parallel reduction in Compute Unified Device Architecture (CUDA). Experimental results show that parallel implementation achieved an average speed up of 30 x over the serial implementation when running on a GPU named GeForce 9500 GT having 32 cores. Texture based retrieval method of CBIR is also evaluated using Recall, Precision, F-measure, True Negative rate, and Accuracy evaluation measures.

1. Fernando, R and Kilgard, M. J. The Cg tutorial the definitive guide to programmable real-time graphics. Addison-Wesley, 2003.
2. Moravanszky, Linear algebra on the GPU, in: W.F. Engel (Ed.), Shader X 2, Wordware Publishing, Texas, 2003.
3. Manocha, Interactive geometric & scientific computations using graphics hardware, SIGGRAPH 2003.
4. Moreland, K. and Angel E. “The FFT on a GPU”. In Proceedings of SIGGRAPH Conference on Graphics Hardware, 112-119, 2003.
5. Mairal, J., Keriven, R. and Chariot, A. “ Fast and efficient dense variational Stereo on GPU”. In Proceedings of International Symposium on 3D Data Processing, Visualization, and Transmission, 97-704, 2006.
6. Yang, R. and Welch, G. “Fast image segmentation and smoothing using commodity graphics hardware”. Journal of Graphics Tools, Vol. 17, (4), 91-100, 2002.
7. Fung, J. and Man, “ OpenVIDIA: Parallel GPU computer vision”. In Proceedings of ACM International Conference on Multimedia, 849-852, 2005.
8. Jang, H., Park, A. and Jung, K. “Neural network implementation using CUDA and OpenMP”. In Proceeding of Computing: Techniques and Applications, (DICTA), IEEE, 155 – 161, 2008.
9. Th. Gevers. “Image segmentation and matching of color-texture objects”. IEEE Trans. on Multimedia, 4(4), 2002.
10. R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, McGraw Hill International Editions, 1995.
11. Rami Al-Tayeche and Ahmed Khalil, “CBIR: Content Based Image Retrieval”Department of Systems and Computer Engineering Faculty of Engineering Carleton University” Tech. Rep. April 4, 2003.
12. Hemant d. Tagare, c. Carl jaffe, james duncan,” Medical Image Database Retrieval”, 1/21/97.
13. Grosky WI. “Iconic Indexing Using Generalized Pattern Matching Techniques”. Computer Vision, Graphics, and ImageProcessing, 1986. 35:383–403.
14. Chang SK. “Picture Indexing and Abstraction Techniques for Pictorial Databases”. IEEE Transactions on Pattern Analysisand Machine Intelligence, 1984. 6(4).
15. Padmashri Suresh ,RMD.Sundaram Aravindhan Arumugam, “Feature Extraction in Compressed Domain for Content Based Image Retrieval”, International Conference on Advanced Computer Theory and Engineering, 10.11.09
16. M. Hatzigiorgaki and A. N. Skodras, “Compressed Domain Image Retrieval: A Comparative Study of Similarity Metrics”, Visual Communications and Image Processing 2003, Touradj Ebrahimi, Thomas Sikora, Editors, Proceedings of SPIE Vol. 5150 (2003).
17. B. Furht, P. Saksobhavivat, “Fast Content-Based Multimedia Retrieval Technique Using Compressed Data,” Proc. SPIE Vol. 3527, pp. 561-571, 1998
18. W.B. Pennebaker and J.L. Mitchell, “JPEG Still Image Data Compression Standard,” Van Nostrand Reinhold, NY, 1993.
19. B.S. Manjunath, J.-R. Ohm, V.V. Vasudevan, and A. Yamada, “Color and Texture Descriptors,” IEEE Trans. Circuits and Systems for Video Technology, Vol. 11, No. 6, pp.703-715, June 2001.
20. V. Castelli and L.D. Bergman (Editors), Image Databases: Search and Retrieval of Digital Imagery, J. Wiley & Sons, NY, 2002
21. Chen, J.Y., Bouman, C.A., and Allebach, J.P., “Fast image database search using tree structured VQ,” Proc. Int. Conf. on Image Processing, USA, Vol.2, pp. 827-830, October 1997.
22. Owens, J. D. Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A. E. and Purcell, T. J. “A survey of general-purpose computation on graphics hardware”. In proceeding of Eurographics, State of the Art Reports, 21–51, 2005.
23. Larsen, E. S., McAllister, D. “Fast Matrix Multiplies using Graphics Hardware”. In Proceeding of International Conference for High Performance Computing and Communications, 159-168, 2001.
24. Trendall C. and Stewart, A. J. “ General calculations using graphics hardware with applications to interactive caustics”. Rendering Techniques 2000: 11th Eurographics Workshop on Rendering, 287-298, 2000.
25. Li, Wei, Wei, Xiaoming, A. and Kaufman, “Implementing lattice boltzmann computation on graphics hardware”. In proceeding of the International Conference for High Performance Computing and Communications , 2001.
26. M. Emmanuel, D.R. Ramesh Babu, Jayashree Jagdale, Pravin Game and G.P. Potdar, “Parallel Approach for Content Based Medical Image Retrieval System”, Journal of Computer Science 6 (11): 1258-1262, 2010.
27. NVIDIA CUDA Programming Guide Version 2.0, available at www.nvidia.com/object/cuda_develop.html.
28. NVIDIA Corporation: NVIDIA CUDA programming guide. Jan 2007, available at http://developer.download.nvidia.com/compute/cuda/2_0/docs/NVIDIA_CUDA_Programming_Guide_2.0.pdf.

Texture Based Image Retrieval CUDA GPU Parallelization GPU Parallelization