1. [1] P. Li and X. Xiao, "Multispectral image segmentation by a multichannel watershed‐based approach," International Journal of Remote Sensing, vol. 28, no. 19, pp. 4429-4452, 2007. [DOI:10.1080/01431160601034910]

2. [2] Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 40, no. 5, pp. 1267-1279, 2010. [DOI:10.1109/TSMCB.2009.2037132]

3. [3] P. W. Pachowicz, "Semi-autonomous evolution of object models for adaptive object recognition," IEEE transactions on systems, man, and cybernetics, vol. 24, no. 8, pp. 1191-1207, 1994. [DOI:10.1109/21.299701]

4. [4] K. Belloulata and J. Konrad, "Fractal image compression with region-based functionality," IEEE transactions on image processing, vol. 11, no. 4, pp. 351-362, 2002. [DOI:10.1109/TIP.2002.999669]

5. [5] S. L. Hartmann and R. L. Galloway, "Depth-buffer targeting for spatially accurate 3-D visualization of medical images," IEEE transactions on medical imaging, vol. 19, no. 10, pp. 1024-1031, 2000. [DOI:10.1109/42.887617]

6. [6] Y. Chen and J. Z. Wang, "A region-based fuzzy feature matching approach to content-based image retrieval," IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 9, pp. 1252-1267, 2002. [DOI:10.1109/TPAMI.2002.1033216]

7. [7] N. R. Pal and S. K. Pal, "A review on image segmentation techniques," Pattern recognition, vol. 26, no. 9, pp. 1277-1294, 1993. [DOI:10.1016/0031-3203(93)90135-J]

8. [8] A. Khotanzad and J.-Y. Chen, "Unsupervised segmentation of textured images by edge detection in multidimensional feature," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, pp. 414-421, 1989. [DOI:10.1109/34.19038]

9. [9] D. K. Panjwani and G. Healey, "Markov random field models for unsupervised segmentation of textured color images," IEEE Transactions on pattern analysis and machine intelligence, vol. 17, no. 10, pp. 939-954, 1995. [DOI:10.1109/34.464559]

10. [10] T. Pavlidis and Y.-T. Liow, "Integrating region growing and edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 225-233, 1990. [DOI:10.1109/34.49050]

11. [11] K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging," IEEE Transactions on image processing, vol. 7, no. 12, pp. 1684-1699, 1998. [DOI:10.1109/83.730380]

12. [12] J. Fan, D. K. Yau, A. K. Elmagarmid, and W. G. Aref, "Automatic image segmentation by integrating color-edge extraction and seeded region growing," IEEE transactions on image processing, vol. 10, no. 10, pp. 1454-1466, 2001. [DOI:10.1109/83.951532]

13. [13] Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, "Lazy snapping," in ACM Transactions on Graphics (ToG), 2004, vol. 23, no. 3, pp. 303-308: ACM. [DOI:10.1145/1015706.1015719]

14. [14] M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, and L. Van Gool, "SEEDS: Superpixels extracted via energy-driven sampling," in European conference on computer vision, 2012, pp. 13-26: Springer. [DOI:10.1007/978-3-642-33786-4_2]

15. [15] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274-2282, 2012. [DOI:10.1109/TPAMI.2012.120]

16. [16] J. Baek, B. Chung, and C. Yim, "Linear Spectral Clustering with Mean Shift Filtering for Superpixel Segmentation."

17. [17] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "Slic superpixels," 2010.

18. [18] P. Dollár and C. L. Zitnick, "Fast edge detection using structured forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 8, pp. 1558 - 1570, 2014. [DOI:10.1109/TPAMI.2014.2377715]

19. [19] J. Shen, X. Hao, Z. Liang, Y. Liu, W. Wang, and L. Shao, "Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm," IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5933-5942, 2016. [DOI:10.1109/TIP.2016.2616302]

20. [20] M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, "Entropy rate superpixel segmentation," in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 2011, pp. 2097-2104: IEEE. [DOI:10.1109/CVPR.2011.5995323]

21. [21] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Kdd, 1996, vol. 96, no. 34, pp. 226-231.

22. [22] P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation," International journal of computer vision, vol. 59, no. 2, pp. 167-181, 2004. [DOI:10.1023/B:VISI.0000022288.19776.77]

23. [23] J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 6, pp. 679-698, 1986. [DOI:10.1109/TPAMI.1986.4767851]

24. [24] P. Dollar, Z. Tu, and S. Belongie, "Supervised learning of edges and object boundaries," in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, pp. 1964-1971: IEEE.

25. [25] M. Van den Bergh, G. Roig, X. Boix, S. Manen, and L. Van Gool, "Online video seeds for temporal window objectness," in Computer Vision (ICCV), 2013 IEEE International Conference on, 2013, pp. 377-384: IEEE. [DOI:10.1109/ICCV.2013.54]

26. [26] F. Y. Shih, Image processing and pattern recognition: fundamentals and techniques. John Wiley & Sons, 2010. [DOI:10.1002/9780470590416]