Comparative analysis of modern automated algorithms image segmentation
Main Article Content
Abstract
Unsupervised image segmentation algorithms based on-mean clustering, expectation-maximization, mean-shift, normalized graph cut, weighted aggregation, statistical region merging, JSEG, HGS and ROI-SEG are considered. The results of segmentation obtained by mentioned algorithms on textural, satellite and natural images are presented. The analysis of quality and segmentation speed of each algorithm realization is performed
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
D. Forsajt and Z. Pons., Komp'yuternoe zrenie [Computer vision], Moscow: Izdatel’skij dom «Vil’yams», 2004, p. 928.
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis”, IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 5, pp. 603–619, May 2002. DOI: 10.1109/34.1000236
J. Shi and J. Malik, “Normalized cuts and image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, Aug. 2000. DOI: 10.1109/34.868688
M. Galun, E. Sharon, R. Basri, and A. Brandt, “Texture segmentation by multiscale aggregation of filter responses and shape elements”, in Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France, 2003, vol. 1, pp. 716–723. DOI:10.1109/ICCV.2003.1238418
R. Nock and F. Nielsen, “Statistical region merging”, IEEE Trans. Pattern Anal. Machine Intell., vol. 26, no. 11, pp. 1452–1458, Nov. 2004. DOI: 10.1109/TPAMI.2004.110
Y. Deng and B. Manjunath, “Unsupervised segmentation of color-texture regions in images and video”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 800–810, Aug. 2001. DOI: 10.1109/34.946985
M. A. Hoang, J.-M. Geusebroek, and A. W. M. Smeulders, “Color Texture Measurement and Segmentation”, Signal Processing, vol. 85, no. 2, pp. 265–275, 2005.
J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust Wide Baseline Stereo from Maximally Stable Extremal Regions”, in Procedings of the British Machine Vision Conference 2002, Cardiff, 2002, pp. 36.1–36.10.
M. Donoser and H. Bischof, “ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results”, in 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007, pp. 1–8. DOI:10.1109/CVPR.2007.383231
A. Hoover, “An experimental comparison of range image segmentation algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 673–689, Jul. 1996. DOI: 10.1109/34.506791
S. Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, Mar. 1982. DOI: 10.1109/TIT.1982.1056489
A. Bhattacharyya, “On a measure of divergence between two statistical populations defined by their probability distributions”, Bulletin of the Calcutta Mathematical Society, no. 35, pp. 99–109, 1943.
The Prague Texture Segmentation Datagenerator and Benchmark. http://mosaic.utia.cas.cz/index.php 19.05.2011
The Berkeley Segmentation Dataset and Benchmark. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/ – 19.05.2011
http://maps.yandex.ru/ 19.05.2011
