The week's pick
Random Articles
Reseach Article
A Survey on Outdoor Scene Image Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 55 - Number 9 |
| Year of Publication: 2012 |
| Authors: Elizabeth Sama Sam, A. Kethsy Prabhavathy, J. Devi Shree |
10.5120/8781-2752
|
Elizabeth Sama Sam, A. Kethsy Prabhavathy, J. Devi Shree . A Survey on Outdoor Scene Image Segmentation. International Journal of Computer Applications. 55, 9 ( October 2012), 5-9. DOI=10.5120/8781-2752
Abstract
Image segmentation is the process of partitioning an image into multiple parts, so that each part or each region corresponds to an object or area of interest that is more significant and easier to analyze. Several general-purpose algorithms and techniques have been developed for image segmentation. This paper describes the different segmentation techniques used to achieve outdoor scene image segmentation. Unlike other surveys that only describe and compare qualitatively different approaches, this survey deals with a real quantitative comparison of the F-measure.
References
- C. Cheng, A. Koschan, D. L. Page, and M. A. Abidi, "Outdoor scene image segmentation based on background recognition and perceptual organization," in Proc. IEEE Trans, vol. 21, no. 3,pp. 1007–1019, March 2012.
- E. Borenstein and E. Sharon, "Combining top-down and bottom-up segmentation," in Proc. IEEEWorkshop Perceptual Org. Comput. Vis. ,CVPR, 2004, pp. 46–53.
- P. Felzenszwalb and D. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vis. , vol. 59, no. 2, pp. 167–181, Sep. 2004.
- J. B. Shi and J. Malik, "Normalized cuts and image segmentation", IEEE Trans. Pattern Anal. Mach. Intell. , vol. 22, no. 8, pp. 888–905, Aug. 2000.
- J. Shotton, J. Winn, C. Rother, and A. Criminisi, "Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context," Int. J. Comput. Vis. ,vol. 81, no. 1, pp. 2–23, Jan. 2009.
- B. Micusik and J. Kosecka, "Semantic segmentation of street scenes by superpixel co-occurrence and 3-D geometry," in Proc. IEEE Workshop VOEC, 2009.
- J. Shotton, M. Johnson, and R. Cipolla, "Semantic texton forests for image categorization and segmentation," in Proc. IEEE CVPR, 2008, pp. 1–8.
- C. Pantofaru, C. Schmid, and M. Hebert, "Object recognition by integrating multiple image segmentations," in Proc. ECCV, 2008, pp. 481–494.
- S. Gould, R. Fulton, and D. Koller, "Decomposing a scene into geometric and semantically consistent regions," in Proc. IEEE ICCV, 2009, pp. 1–8.
- S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, "Multi-class segmentation with relative location prior," Int. J. Comput. Vis. , vol. 80, no. 3, pp. 300–316, Dec. 2008.
- M. Maire, P. Arbelaez, C. C. Fowlkes, and J. Malik, "Using contours to detect and localize junctions in natural images," in Proc. IEEE CVPR, 2008, pp. 1–8.
- D. Hoiem, A. N. Stein, A. A. Efros, and M. Hebert, "Recovering occlusion boundaries from a single image," in Proc. IEEE ICCV, 2007, pp. 1–8.
- Solomon C. J. and Breckon T. P. , Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab, Wiley-Blackwell,2010.
- P. Dollar, Z. W. Tu, and S. Belongie, "Supervised learning of edges and object boundaries," in Proc. IEEE CVPR, 2006, vol. 2, pp. 1964–1971.
- T. Malisiewicz and A. A. Efros, "Improving spatial support for objects via multiple segmentations," in Proc. BMVC, 2007.
Index Terms
Keywords