Abstract
Image segmentation is required to run fast and without supervision to speed up subsequent processes such as object recognition or other high level tasks. General purpose computing on the GPU is a powerful tool to perform efficient image processing and has been applied to the image segmentation problem. However, state-of-the-art approaches still perform parts of the computations on the CPU requiring costly data exchange with the main memory. In this paper we suggest a fully unsupervised color image segmentation that runs completely on the GPU including the calculation of region features. We compare our results to a popular CPU-based and a recent GPU-based method and report a computation time advantage.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Image Segmentation
- Color Space
- Cluster Centroid
- Single Instruction Multiple Data
- Color Image Segmentation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
NVIDIA CUDA (Compute Unified Device Architecture) C - Programming Guide (2012), http://www.nvidia.com/content/cuda/cuda-documentation.html
Abramov, A., Kulvicius, T.: Real-time Image Segmentation on a GPU. In: Facing the Multicore Challenge, vol. 5, pp. 3–5 (2011)
Aziz, M.Z., Mertsching, B.: Fast and Robust Generation of Feature Maps for Region-based Visual Attention. IEEE Trans. on Image Proc. 17(5), 633–644 (2008)
Batcher, K.E.: Sorting Networks and Their Applications. In: Spring Joint Computer Conference, AFIPS 1968, New York, USA, pp. 307–314 (1968)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics), ch. 9, vol. 4, pp. 424–427. Springer (2007)
Cates, J.E., Lefohn, A.E., Whitaker, R.T.: GIST: An Interactive, GPU-based Level Set Segmentation Tool for 3D Medical Images. Medical Image Analysis 8(3), 217–231 (2004)
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image Segmentation: Advances and Prospects. Pattern Recognition 34(12), 2259–2281 (2001)
Coleman, G.B., Andrews, H.C.: Image Segmentation by Clustering. IEEE 67, 773–785 (1979)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn., ch. 21, pp. 498–524. The MIT Press (2001)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) (2007), Results, http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Farivar, R., Rebolledo, D., Chan, E., Campbell, R.: A Parallel Implementation of K-Means Clustering on GPUs. In: PDPTA, pp. 1–6 (2008)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Flynn, M.J.: Some Computer Organizations and Their Effectiveness. IEEE Transactions on Computers C-21(9), 948–960 (1972)
Fulkerson, B., Soatto, S.: Really Quick Shift: Image Segmentation on a GPU. In: ECCV Workshops, vol. i, pp. 8–11 (2010)
Hong-tao, B., Li-li, H., Dan-tong, O., Zhan-shan, L., He, L.: K-Means on Commodity GPUs with CUDA. CSIE 3, 651–655 (2009)
Kalentev, O., Rai, A., Kemnitz, S., Schneider, R.: Connected Component Labeling on a 2D Grid Using CUDA. Journal of Parallel and Distributed Computing 71, 615–620 (2011)
Roberts, M., Packer, M., Sousa, M., Mitchell, J.R.: A Work-Efficient GPU Algorithm for Level Set Segmentation. In: Conference on High Performance Graphics, HPG 2010, pp. 123–132 (2010)
Sengupta, S., Harris, M., Garland, M.: Efficient Parallel Scan Algorithms for GPUs. NVIDIA Technical Report NVR-2008-003 66(1), 1–17 (2008)
Shalom, S.A.A., Dash, M., Tue, M.: Efficient K-Means Clustering Using Accelerated Graphics Processors. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 166–175. Springer, Heidelberg (2008)
Soman, J., Kishore, K., Narayanan, P.J.: A Fast GPU Algorithm for Graph Connectivity. In: IPDPS Workshops, pp. 1–8 (2010)
Stone, J.E., Gohara, D., Shi, G.: OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems. Computing in Science & Engineering 12(3), 66–73 (2010)
Tomasi, C., Manduchi, R.: Bilateral Filtering for Gray and Color Images. In: ICCV, pp. 839–846 (1998)
Tünnermann, J., Mertsching, B.: Continuous Region-based Processing of Spatiotemporal Saliency. In: VISAPP, pp. 230–239 (2012)
Vedaldi, A., Soatto, S.: Quick Shift and Kernel Methods for Mode Seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)
Zechner, M., Granitzer, M.: Accelerating K-Means on the Graphics Processor via CUDA. In: INTENSIVE, pp. 7–15 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Backer, M., Tünnermann, J., Mertsching, B. (2013). Parallel k-Means Image Segmentation Using Sort, Scan and Connected Components on a GPU. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol 7686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35893-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-35893-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35892-0
Online ISBN: 978-3-642-35893-7
eBook Packages: Computer ScienceComputer Science (R0)