Structure-Sensitive Superpixels via Geodesic Distance
Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by...
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| Published in | International journal of computer vision Vol. 103; no. 1; pp. 1 - 21 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.05.2013
Springer Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach. |
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| AbstractList | Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach. Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd's algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach. Keywords Superpixel segmentation * Geodesic distance * Iterative optimization * Structure-sensitivity Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd's algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach.[PUBLICATION ABSTRACT] |
| Audience | Academic |
| Author | Wang, Peng Zha, Hongbin Wang, Jingdong Zeng, Gang Gan, Rui |
| Author_xml | – sequence: 1 givenname: Peng surname: Wang fullname: Wang, Peng organization: Key Laboratory on Machine Perception, Peking University – sequence: 2 givenname: Gang surname: Zeng fullname: Zeng, Gang email: g.zeng@ieee.org organization: Key Laboratory on Machine Perception, Peking University – sequence: 3 givenname: Rui surname: Gan fullname: Gan, Rui organization: School of Mathematical Sciences, Peking University – sequence: 4 givenname: Jingdong surname: Wang fullname: Wang, Jingdong organization: Microsoft Research Asia – sequence: 5 givenname: Hongbin surname: Zha fullname: Zha, Hongbin organization: Key Laboratory on Machine Perception, Peking University |
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| Cites_doi | 10.1007/978-3-540-72823-8_16 10.1109/ICCV.2009.5459246 10.1109/ICPR.2006.969 10.1007/978-3-642-15555-0_16 10.1137/040617364 10.1109/34.1000236 10.1109/ICASSP.2007.366264 10.1109/ICCV.2005.112 10.1109/TPAMI.2004.1273918 10.1023/B:VISI.0000022288.19776.77 10.1109/34.87344 10.1109/ICCV.2005.107 10.1109/TPAMI.2009.96 10.1109/CVPR.2008.4587420 10.1109/CVPR.2007.383017 10.1109/CVPR.2006.298 10.1109/CVPR.2008.4587371 10.1007/978-3-642-15552-9_35 10.1007/s11263-010-0327-9 10.1109/CVPR.2008.4587471 10.1007/s11227-006-0002-7 10.1109/DEXA.2009.39 10.1007/978-3-540-76858-6_5 10.1109/ICCV.2001.937655 10.1007/978-1-4757-1904-8 10.1109/CVPR.2010.5540073 10.1007/11744023_27 10.1109/CVPR.2006.326 10.1073/pnas.93.4.1591 10.1007/978-3-540-70706-6_5 10.1007/11744023_1 10.1109/CVPR.2009.5206536 10.1109/ICCV.2009.5459175 10.1023/A:1018647011077 10.1109/ICCV.2009.5459472 10.21236/ADA478319 10.3233/IDA-2004-8403 10.5244/C.21.55 10.1109/CVPR.2009.5206707 10.1007/978-3-540-88682-2_9 10.1023/A:1005269208310 10.1109/34.868688 10.1109/TIT.1982.1056489 10.1016/j.jcp.2005.08.005 |
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| Keywords | Structure-sensitivity Iterative optimization Superpixel segmentation Geodesic distance Computer vision Closure Image processing Data compression Iterative method Image compression Computational complexity Geodesic flow Optimization Computational geometry Image segmentation Image analysis Efficiency Database Geodesic Homogeneity Gradient flow Pixel |
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| References | LiY.ChungS. M.Parallel bisecting k-means with prediction clustering algorithmThe Journal of Supercomputing2007391937 Wang, J., Jia, Y., Hua, X. S., Zhang, C.,& Quan, L. (2008). Normalized tree partitioning for image segmentation. In CVPR. Lucas, B., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of the DARPA image understanding workshop (pp. 121–130). SavaresiSMBoleyDA comparative analysis on the bisecting K-means and the PDDP clustering algorithmsIntelligent Data Analysis200484345362 Kim, J., Shim, K. H., & Choi, S. (2007). Soft geodesic kernel k-means. In ICASSP (pp. 429–432). Wang, S., Lu, H., Yang, F.,& Yang, M. H. (2011). Superpixel tracking. In ICCV (pp. 1323–1330). Gulshan, V., Rother, C., Criminisi, A., Blake, A., & Zisserman, A. (2010). Geodesic star convexity for interactive image segmentation. In CVPR (pp. 3129–3136). Hoiem, D., Efros, A. A., & Hebert, M. (2005). Geometric context from a single image. In ICCV (pp. 654–661). Feil, B., & Abonyi, J. (2007). Geodesic distance based fuzzy clustering. Lecture notes in computer science, soft computing in industrial applications (pp. 50–59). He, X., Zemel, R. S., & Ray, D. (2006). Learning and incorporating top-down cues in image segmentation. In ECCV (Vol. 1, pp. 338–351). Dollár, P., Tu, Z., & Belongie, S. (2006). Supervised learning of edges and object boundaries. In CVPR (Vol. 2, pp. 1964–1971). SethianJA fast marching level set method for monotonically advancing frontsProceedings of the National Academy of Sciences1996931591169413740100852.6505510.1073/pnas.93.4.1591 Meyer, F., & Maragos, P. (1999). Multiscale morphological segmentations based on watershed, flooding, and eikonal PDE. In Scale space (pp. 351–362). Moore, A. P., Prince, S. J. D., & Warrell, J. (2010). “lattice cut”—Constructing superpixels using layer constraints. In CVPR (pp. 2117–2124). Alpert, S., Galun, M., Basri, R., & Brandt, A. (2007). Image segmentation by probabilistic bottom-up aggregation and cue integration. In CVPR. PeyréGPéchaudMKerivenRCohenLDGeodesic methods in computer vision and graphicsFoundations and Trends in Computer Graphics and Vision201053–4197397 Bai, X., & Sapiro, G. (2007). A geodesic framework for fast interactive image and video segmentation and matting. In ICCV (pp. 1–8). DuQ.EmelianenkoM.JuL.Convergence of the lloyd algorithm for computing centroidal voronoi tessellationsSIJNA: SIAM Journal on Numerical Analysis200644102119 MartinDRFowlkesCMalikJLearning to detect natural image boundaries using local brightness, color, and texture cuesIEEE Transactions on Pattern Analysis and Machine Intelligence200426553054910.1109/TPAMI.2004.1273918 Veksler, O., Boykov, Y.,& Mehrani, P. (2010). Superpixels and supervoxels in an energy optimization framework. In ECCV (Vol. 5, pp. 211–224). VincentL.SoilleP.Watersheds in digital spaces: An efficient algorithm based on immersion simulationsIEEE Transactions on Pattern Analysis and Machine Intelligence1991136583598 Maire, M., Arbelaez, P., Fowlkes, C., & Malik, J. (2008). Using contours to detect and localize junctions in natural images. In CVPR. Jolliffe, I. T. (1986). Principal component analysis. In Principal component analysis. New York: Springer. Xiao, J.,& Quan, L. (2009). Multiple view semantic segmentation for street view images. In ICCV (pp. 686–693). ShiJMalikJNormalized cuts and image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence200022888890510.1109/34.868688 YatzivL.BartesaghiA.SapiroG.O(n) implementation of the fast marching algorithmJournal of Computational Physics,20062122393393 Moore, A. P., Prince, S. J. D., Warrell, J., Mohammed, U., & Jones G. (2009). Scene shape priors for superpixel segmentation. In ICCV (pp. 771–778). Levinshtein, A., Sminchisescu, C., & Dickinson, S. J. (2010). Optimal contour closure by superpixel grouping. In ECCV (Vol. 2, pp. 429–493). Malisiewicz, T., & Efros, A. A. (2007). Improving spatial support for objects via multiple segmentations. In BMVC. Martin, D. R., 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. In ICCV (pp. 416–425). Tai, X. C., Hodneland, E., Weickert, J., Bukoreshtliev, N. V., Lundervold, A.,& Gerdes, H. H. (2007). Level set methods for watershed image segmentation. In Scale-space (pp. 178–190). Liu, C., Yuen, J., & Torralba, A. (2009). Nonparametric scene parsing: Label transfer via dense scene alignment. In CVPR (pp. 1972– 1979). SethianJ. A.A fast marching level set method for monotonically advancing frontsProceedings of the National Academy of Sciences1996b93415911595 Moore, A. P., Prince, S., Warrell, J., Mohammed, U., & Jones, G. (2008). Superpixel lattices. In CVPR. HyvärinenAThe fixed-point algorithm and maximum likelihood estimation for independent component analysisNeural Processing Letters19991011510.1023/A:1018647011077 Muhr, M., & Granitzer, M. (2009). Automatic cluster number selection using a split and merge K-means approach. In A. M. Tjoa & R. Wagner (Eds)., DEXA workshops (pp. 363–367). IEEE Computer Society. HarelJKochCPeronaPSchölkopfBPlattJCHoffmanTGraph-based visual saliencyNIPS2006Cambridge, MAMIT Press545552 Rasmussen, C. (2007). Superpixel analysis for object detection and tracking with application to UAV imagery. In Advances in visual computing (Vol. I, pp. 46–55). Kaufhold, J. P., Collins, R., Hoogs, A., & Rondot, P. (2006). Recognition and segmentation of scene content using region-based classification. In ICPR (Vol. 1, pp. 755–760). MicusíkBKoseckáJMulti-view superpixel stereo in urban environmentsInternational Journal of Computer Vision201089110611910.1007/s11263-010-0327-9 Nwogu, I., & Corso, J. J. (2008). (bp)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document}: Beyond pairwise belief propagation labeling by approximating kikuchi free energies. In CVPR. Radhakrishna, A., Appu, S., Kevin, S., Aurelien, L., Pascal, F.,& Susstrunk, S. (2010). Slic superpixels. Technical Report 149300 EPFL (June), p. 15. Russell, B. C., Freeman, W. T., Efros, A. A., Sivic, J.,& Zisserman, A. (2006). Using multiple segmentations to discover objects and their extent in image collections. In CVPR (Vol. 2, pp. 1605–1614). Criminisi, A., Sharp, T., & Blake, A. (2008). Geos: Geodesic image segmentation. In ECCV (pp. 99–112). LloydSPLeast squares quantization in PCMIEEE Transactions on Information Theory19822812813765180710.1109/TIT.1982.1056489 Arbelaez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2009). From contours to regions: An empirical evaluation. In CVPR (pp. 2294–2301). Fulkerson, B., Vedaldi, A., & Soatto, S. (2009). Class segmentation and object localization with superpixel neighborhoods. In ICCV (pp. 670–677). Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S. J., & Siddiqi, K. (2009b). Turbopixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2290–2297. ComaniciuDMeerPMean shift: A robust approach toward feature space analysisIEEE Transactions on Pattern Analysis and Machine Intelligence200224560361910.1109/34.1000236 Shotton, J., Winn, J. M., Rother, C.,& Criminisi, A. (2006). TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV (Vol. 1, pp. 1–15). Levinshtein, A., Dickinson, S. J., & Sminchisescu, C. (2009a). Multiscale symmetric part detection and grouping. In ICCV (pp. 2162–2169). FelzenszwalbPFHuttenlocherDPEfficient graph-based image segmentationInternational Journal of Computer Vision200459216718110.1023/B:VISI.0000022288.19776.77 Mori, G. (2005). Guiding model search using segmentation. In ICCV (pp. 1417–1423). 588_CR19 588_CR18 DR Martin (588_CR28) 2004; 26 J Harel (588_CR12) 2006 588_CR20 588_CR26 588_CR25 588_CR27 588_CR22 588_CR21 588_CR23 588_CR3 588_CR2 588_CR1 588_CR8 588_CR7 588_CR6 A Hyvärinen (588_CR15) 1999; 10 588_CR5 PF Felzenszwalb (588_CR9) 2004; 59 D Comaniciu (588_CR4) 2002; 24 J Sethian (588_CR43) 1996; 93 588_CR51 588_CR50 588_CR53 588_CR52 588_CR14 588_CR17 588_CR16 588_CR11 588_CR10 588_CR13 588_CR40 588_CR41 SP Lloyd (588_CR24) 1982; 28 588_CR48 588_CR47 588_CR49 588_CR44 588_CR46 G Peyré (588_CR38) 2010; 5 588_CR29 B Micusík (588_CR31) 2010; 89 SM Savaresi (588_CR42) 2004; 8 J Shi (588_CR45) 2000; 22 588_CR30 588_CR37 588_CR36 588_CR39 588_CR33 588_CR32 588_CR35 588_CR34 |
| References_xml | – ident: 588_CR47 doi: 10.1007/978-3-540-72823-8_16 – ident: 588_CR34 doi: 10.1109/ICCV.2009.5459246 – ident: 588_CR17 doi: 10.1109/ICPR.2006.969 – ident: 588_CR48 doi: 10.1007/978-3-642-15555-0_16 – ident: 588_CR7 doi: 10.1137/040617364 – volume: 24 start-page: 603 issue: 5 year: 2002 ident: 588_CR4 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.1000236 contributor: fullname: D Comaniciu – ident: 588_CR18 doi: 10.1109/ICASSP.2007.366264 – ident: 588_CR35 doi: 10.1109/ICCV.2005.112 – volume: 26 start-page: 530 issue: 5 year: 2004 ident: 588_CR28 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2004.1273918 contributor: fullname: DR Martin – volume: 59 start-page: 167 issue: 2 year: 2004 ident: 588_CR9 publication-title: International Journal of Computer Vision doi: 10.1023/B:VISI.0000022288.19776.77 contributor: fullname: PF Felzenszwalb – ident: 588_CR49 doi: 10.1109/34.87344 – ident: 588_CR14 doi: 10.1109/ICCV.2005.107 – ident: 588_CR21 doi: 10.1109/TPAMI.2009.96 – ident: 588_CR26 doi: 10.1109/CVPR.2008.4587420 – ident: 588_CR1 doi: 10.1109/CVPR.2007.383017 – ident: 588_CR6 doi: 10.1109/CVPR.2006.298 – ident: 588_CR37 doi: 10.1109/CVPR.2008.4587371 – ident: 588_CR50 – ident: 588_CR20 doi: 10.1007/978-3-642-15552-9_35 – volume: 89 start-page: 106 issue: 1 year: 2010 ident: 588_CR31 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-010-0327-9 contributor: fullname: B Micusík – ident: 588_CR32 – ident: 588_CR33 doi: 10.1109/CVPR.2008.4587471 – ident: 588_CR22 doi: 10.1007/s11227-006-0002-7 – ident: 588_CR36 doi: 10.1109/DEXA.2009.39 – ident: 588_CR40 doi: 10.1007/978-3-540-76858-6_5 – ident: 588_CR29 doi: 10.1109/ICCV.2001.937655 – ident: 588_CR16 doi: 10.1007/978-1-4757-1904-8 – ident: 588_CR51 – volume: 5 start-page: 197 issue: 3–4 year: 2010 ident: 588_CR38 publication-title: Foundations and Trends in Computer Graphics and Vision contributor: fullname: G Peyré – ident: 588_CR11 doi: 10.1109/CVPR.2010.5540073 – ident: 588_CR13 doi: 10.1007/11744023_27 – ident: 588_CR41 doi: 10.1109/CVPR.2006.326 – ident: 588_CR44 doi: 10.1073/pnas.93.4.1591 – start-page: 545 volume-title: NIPS year: 2006 ident: 588_CR12 contributor: fullname: J Harel – ident: 588_CR8 doi: 10.1007/978-3-540-70706-6_5 – ident: 588_CR46 doi: 10.1007/11744023_1 – ident: 588_CR23 doi: 10.1109/CVPR.2009.5206536 – ident: 588_CR10 doi: 10.1109/ICCV.2009.5459175 – volume: 10 start-page: 1 issue: 1 year: 1999 ident: 588_CR15 publication-title: Neural Processing Letters doi: 10.1023/A:1018647011077 contributor: fullname: A Hyvärinen – ident: 588_CR19 doi: 10.1109/ICCV.2009.5459472 – ident: 588_CR3 doi: 10.21236/ADA478319 – volume: 8 start-page: 345 issue: 4 year: 2004 ident: 588_CR42 publication-title: Intelligent Data Analysis doi: 10.3233/IDA-2004-8403 contributor: fullname: SM Savaresi – ident: 588_CR27 doi: 10.5244/C.21.55 – ident: 588_CR2 doi: 10.1109/CVPR.2009.5206707 – ident: 588_CR5 doi: 10.1007/978-3-540-88682-2_9 – ident: 588_CR25 – ident: 588_CR30 doi: 10.1023/A:1005269208310 – volume: 22 start-page: 888 issue: 8 year: 2000 ident: 588_CR45 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.868688 contributor: fullname: J Shi – volume: 93 start-page: 1591 year: 1996 ident: 588_CR43 publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.93.4.1591 contributor: fullname: J Sethian – ident: 588_CR39 – ident: 588_CR52 – volume: 28 start-page: 128 year: 1982 ident: 588_CR24 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1982.1056489 contributor: fullname: SP Lloyd – ident: 588_CR53 doi: 10.1016/j.jcp.2005.08.005 |
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| Title | Structure-Sensitive Superpixels via Geodesic Distance |
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