Abstract
Graph-based method has become one of the major trends in image segmentation. In this paper, we focus on how to build the affinity matrix which is one of the key issues in graph-based color image segmentation. Four different metrics are integrated in order to build an effective affinity matrix for segmentation. First, the quaternion-based color distance is utilized to measure color differences between color pixels and the oversegmented regions (superpixels), which is more accurate than the commonly used Euclidean distance. In order to describe the superpixels well, especially for texture images, we combine the mean and the variance information to represent the superpixels. Then the image boundary information is used to merge the oversegmented regions to preserve the image edge and reduce the computational complexity. An object for recognition may be cut into nonadjacent sub-parts by clutter or shadows, the affinities between adjacent and nonadjacent superpixels are computed in our study. This feature of affinity is not considered in other methods which only consider the similarity of adjacent regions. Experimental results on the Berkeley segmentation dataset (BSDS) and Weizmann segmentation evaluation datasets demonstrate the superiority of the proposed approach compared with some existing popular image segmentation methods.
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Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intel 34(11):2274–2282
Alpert S, Galun M, Basri R, Brandt A (2007) Image segmentation by probabilistic bottom-up aggregation and cue integration. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, pp 1-8
Arbelaez P, Maire M, Fowlkes C, Malik J (2009) From contours to regions: An empirical evaluation. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, pp 2294-2301
Cai C, Mitra SK (2000) A normalized color difference edge detector based on quaternion representation. Paper presented at the International Conference on Image Processing, pp 816-819
Collins MD, Xu J, Grady L, Singh V (2012) Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, pp 1656-1663
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intel 24(5):603–619
Cour T, Benezit F, Shi J (2005) Spectral segmentation with multiscale graph decomposition. Paper presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1124-1131
Deng Y, Manjunath B (2001) Unsupervised segmentation of color-texture regions in images and video. IEEE Trans Pattern Anal Mach Intel 23(8):800–810
Donoser M, Urschler M, Hirzer M, Bischof H (2009) Saliency driven total variation segmentation. Paper presented at the IEEE 12th International Conference on Computer Vision, pp 817-824
Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181
Freixenet J, Muñoz X, Raba D, Martí J, Cufí X (2002) Yet another survey on image segmentation: Region and boundary information integration. In: Computer Vision—ECCV 2002. Springer, pp 408-422
Gastal ES, Oliveira MM (2012) Adaptive manifolds for real-time high-dimensional filtering. ACM Trans Graph (TOG) 31(4):1–13
Hamilton WR (1866) Elements of quaternions. Longmans, Green, & Company, London
Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 7(12):1684–1699
Jahne B (2002) Digital image processing. Springer, Berlin
Jin L, Liu H, Xu X, Song E (2012) Improved direction estimation for Di Zenzo’s multichannel image gradient operator. Pattern Recogn 45(12):4300–4311
Jin L, Liu H, Xu X, Song E (2013) Quaternion-based impulse noise removal from color video sequences. IEEE Trans Circ Syst Video Technol 23(5):741–755
Kim TH, Lee KM, Lee SU (2013) Learning full pairwise affinities for spectral segmentation. IEEE Trans Pattern Anal Mach Intel 35(7):1690–1703
Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K (2009) Turbopixels: fast superpixels using geometric flows. IEEE Trans Pattern Analy Mach Intel 31(12):2290–2297
Li Z, Wu X-M, Chang S-F (2012) Segmentation using superpixels: A bipartite graph partitioning approach. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, pp 789-796
Makrogiannis S, Economou G, Fotopoulos S (2005) A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans Syst Man Cybernet B Cybernet 35(1):44–53
Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Paper presented at the Eighth IEEE International Conference on Computer Vision, pp 416-423
Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intel 26(5):530–549
Meilǎ M (2005) Comparing clusterings: an axiomatic view. Paper presented at the Proceedings of the 22nd international conference on Machine learning, pp 577-584
Mobahi H, Rao SR, Yang AY, Sastry SS, Ma Y (2011) Segmentation of natural images by texture and boundary compression. Int J Comput Vis 95(1):86–98
Nguyen HT, Worring M, Dev A (2000) Detection of moving objects in video using a robust motion similarity measure. IEEE Trans Image Process 9(1):137–141
Pei S-C, Cheng C-M (1997) A novel block truncation coding of color images using a quaternion-moment-preserving principle. IEEE Trans Commun 45(5):583–595
Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850
Sangwine SJ (1996) Fourier transforms of colour images using quaternion or hypercomplex, numbers. Electron Lett 32(21):1979–1980
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intel 22(8):888–905
Subakan ÖN, Vemuri BC (2011) A quaternion framework for color image smoothing and segmentation. Int J Comput Vis 91(3):233–250
Tao W, Jin H, Zhang Y (2007) Color image segmentation based on mean shift and normalized cuts. IEEE Trans Syst Man Cybernet B Cybernet 37(5):1382–1389
Tilton JC (1998) Image segmentation by region growing and spectral clustering with natural convergence criterion. Paper presented at the International geoscience and remote sensing symposium, pp 1766-1768
Tilton JC, Tarabalka Y, Montesano PM, Gofman E (2012) Best merge region-growing segmentation with integrated nonadjacent region object aggregation. IEEE Trans Geosci Remote Sens 50(11):4454–4467
Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intel 29(6):929–944
Vedaldi A, Soatto S (2008) Quick shift and Kernel methods for mode seeking. In: Forsyth D, Torr P, Zisserman A (eds) Computer vision – ECCV 2008, vol 5305. lecture notes in computer science. Springer, Berlin, pp 705–718. doi:10.1007/978-3-540-88693-8_52
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Wang C, Guo Y, Zhu J, Wang L, Wang W (2014) Video object co-segmentation via subspace clustering and quadratic pseudo-boolean optimization in an MRF framework. IEEE Trans Multimed 16(4):903–916
Wang J, Jia Y, Hua X-S, Zhang C, Quan L (2008) Normalized tree partitioning for image segmentation. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, pp 1-8
Wang S, Siskind JM (2003) Image segmentation with ratio cut. IEEE Trans Pattern Anal Mach Intel 25(6):675–690
Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intel 15(11):1101–1113
Xia T, Cao J, Zhang Y, Li J (2009) On defining affinity graph for spectral clustering through ranking on manifolds. Neurocomputing 72(13):3203–3211
Zhang X, Li J, Yu H (2011) Local density adaptive similarity measurement for spectral clustering. Pattern Recogn Lett 32(2):352–358
Zhu S-Y, Plataniotis KN, Venetsanopoulos AN (1999) Comprehensive analysis of edge detection in color image processing. Opt Eng 38(4):612–625
Acknowledgments
The authors would like to thank the anonymous reviewers for their insightful suggestions, and Dr. Chih-Cheng Hung for his valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61370181, 61075010 and 61370179), and National Key Technology Research and Development Program of China (Grant No. 2012BAI23B07).
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Li, X., Jin, L., Song, E. et al. An integrated similarity metric for graph-based color image segmentation. Multimed Tools Appl 75, 2969–2987 (2016). https://doi.org/10.1007/s11042-014-2416-1
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DOI: https://doi.org/10.1007/s11042-014-2416-1