Computer Vision for the Structured Representation and Stylisation of Visual Media Collections

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Tinghuai Wang: Computer Vision for the Structured Representation and Stylisation of Visual Media Collections. PhD Thesis, Department of Electronic Engineering, University of Surrey, UK, July 2012.



The proliferation of digital cameras in commodity consumer devices, and the social trend for casually capturing and sharing media, has led to an explosive growth in personal visual media collections. However this wealth of digital material is infrequently accessed beyond the point of initial capture or sharing, and often lies dormant gathering digital dust in the media repository. This thesis proposes novel Computer Vision and Computer Graphics techniques to release the value in personal media collections - investigating new ways to stylise and present images and video in such collections. First, personal visual media tends to be shot casually in varied and challenging capture conditions by amateur operators. This necessitates some interactive manipulation prior to presentation. This thesis contributes a novel solution for editing such amateur home video into succinct clips of this nature, using a parse-tree representation of the video editing process. We also enable interactive manipulation of still images through a novel object segmentation algorithm dubbed TouchCut which enables object selection with a single touch and is intended for direct media manipulation on commodity media capture devices such as touch-screen digital cameras. Second, there is an interaction barrier to digital media. The casual nature of personal media encourages the capture of significantly larger collections than traditional media. This thesis explores the application of artistic stylisation to create digital ambient displays (DADs) of personal media in the style of cartoons, paintings and paper-cut out. Underpinning this contribution are two new algorithms for video segmentation that enforce temporal coherences within the stylised video. Furthermore, we explore how structuring the media collection hierarchically within the DAD can promote interest and engagement with the collection. Third, personal media collections often contains images of friends or family members. The artistic stylisation of such content using existing approaches rarely results in acceptable output. We propose a novel example-based approach to portrait stylisation driven by a high level model of facial structure that gives rise to improved aesthetics and enables the example-based rendering of a diverse range of portrait styles. This work was supported by the Hewlett Packard Laboratories Innovation Research Program.

Extended Abstract


Used References

[1] D. Adalsteinsson and J. Sethian. A fast level set method for propagating interfaces. Journal of Computational Physics, pages 269–277, 1995.

[2] A. Agarwala, A. Hertzmann, D. Salesin, and S. M. Seitz. Keyframe-based tracking for rotoscoping and animation. ACM Trans. Graph., 23(3):584–591, 2004.

[3] M. A. Ahmed, F. Pitie, and A. Kokaram. Extraction of non-binary blotch mattes. In Proc. ICIP, pages 2757–2760, 2009.

[4] M. Al-Hames, B. H ̈ornler, R. M ̈uller, J. Schenk, and G. Rigoll. Automatic multi- modal meeting camera selection for video-conferences and meeting browsers. In Proc. ICME, pages 2074–2077, 2007.

[5] K. Alahari, P. Kohli, and Torr. Reduce, reuse & recycle: Efficiently solving multi-label mrfs. In Proc. CVPR, pages 1–8, 2008.

[6] P. Arbelaez and L. D. Cohen. Constrained image segmentation from hierarchical boundaries. In Proc. CVPR, pages 1–8, 2008.

[7] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchi- cal image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 33(5):898–916, 2011.

[8] D. Arijon. Grammar of the Film Language. Silman-James Press, 1991.

[9] N. Arksey. Exploring the design space for concurrent use of personal and large displays for in-home collaboration. Master’s thesis, University of British Columbia, Aug, 2007.

[10] C. Armstrong, B. Price, and W. Barrett. Interactive segmentation of image volumes with live surface. Computers & Graphics, 31(2):212–229, 2007.

[11] X. Bai and G. Sapiro. A geodesic framework for fast interactive image and video segmentation and matting. In Proc. ICCV, pages 1–8, 2007.

[12] X. Bai, J. Wang, and G. Sapiro. Dynamic color flow: A motion-adaptive color model for object segmentation in video. In Proc. ECCV, pages 617–630, 2010.

[13] X. Bai, J. Wang, D. Simons, and G. Sapiro. Video snapcut: robust video object cutout using localized classifiers. In Proc. SIGGRAPH, pages 1–11, 2009.

[14] J. A. Bangham, S. E. Gibson, and R. Harvey. The art of scale-space. In Proc. BMVC, pages 569–578, 2003.

[15] S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color- and texture-based image segmentation using em and its application to content-based image retrieval. In Proc. ICCV, pages 675–682, 1998.

[16] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell., 24:509–521, 2002.

[17] M. Black and P. Anandan. A framework for the robust estimation of optical flow. In Proc. ICCV, pages 231–236, 1993.

[18] A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr. Interactive image seg- mentation using an adaptive gmmrf model. In Proc. ECCV, pages 428–441, 2004.

[19] V. Blanz and T. Vetter. A morphable model for the synthesis of 3d faces. In Proc. SIGGRAPH, pages 187–194, 1999.

[20] A. Bousseau, F. Neyret, J. Thollot, and D. Salesin. Video watercolorization using bidirectional texture advection. ACM Trans. Graph., 26(3):104:1–7, 2007.

[21] Y. Boykov and G. Funka-Lea. Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision, 2(70):109–131, 2006.Bibliography 193

[22] Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In Proc. ICCV, pages 105–112. IEEE, 2001.

[23] Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26:1124–1137, 2004.

[24] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 23:1222–1239, 2001.

[25] W. Brendel and S. Todorovic. Video object segmentation by tracking regions. In Proc. ICCV, 2009.

[26] X. Bresson, P. Vandergheynst, and J. Thiran. A priori information in image segmentation: Energy functional based on shape statistical model and image information. In Proc. ICIP, pages 425–428, 2003.

[27] T. Brox and D. Cremers. On the statistical interpretation of the piecewise smooth mumford-shah functional. In Proc. SSVM, pages 203–213, 2007.

[28] T. Brox and J. Weickert. Level set segmentation with multiple regions. IEEE Trans. on Image Process., 15(10):3213–3218, 2006.

[29] Richardt C. Colour videos with depth: acquisition, processing and evaluation. PhD thesis, Cambridge University, March 2012.

[30] J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679–698, 1986.

[31] V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. In Proc. ICCV, pages 694–699, 1995.

[32] T. Chan and L. Vese. Active contours without edges. IEEE Trans. Image Process., pages 266–277, 2001.

[33] H. Chen, Z. Liu, C. Rose, Y. Xu, H.-Y. Shum, and D. Salesin. Example-based composite sketching of human portraits. In Proc. NPAR, pages 95–153, 2004.

[34] H. Chen, N.-N. Zheng, L. Liang, Y. Li, Y.-Q. Xu, and H.-Y. Shum. Pictoon: a personalized image-based cartoon system. In Proc. MM, pages 171–178, 2002.

[35] J. Chen, C.A Bouman, and J. C. Dalton. Hierarchical browsing and search of large image databases. IEEE Trans. Image Process., 9:442–455, 2000.

[36] P. Chockalingam, S. Nalin Pradeep, and S. Birchfield. Adaptive fragments-based tracking of non-rigid objects using level sets. In Proc. ICCV, pages 1530–1537, 2009.

[37] P. Chopra and J. Meyer. Modeling an infinite emotion space for expressionistic cartoon face animation. In Computer Graphics and Imaging, pages 13–18, 2003.

[38] J. Collomosse. Higher Level Techniques for the Artistic Rendering of Images and Video. PhD thesis, University of Bath, May 2004.

[39] J. Collomosse and P. M. Hall. Painterly rendering using image salience. In Proc. EGUK, pages 122–128, 2002.

[40] J. Collomosse and P. M. Hall. Cubist style rendering from photographs. IEEE Trans. Vis. Comput. Graph., 4(9):443–453, 2003.

[41] J. Collomosse and P. M. Hall. Genetic paint: A search for salient paintings. In Proc. EvoMUSART, pages 437–447, 2005.

[42] J. Collomosse, D. Rowntree, and P. M. Hall. Stroke surfaces: Temporally coherent artistic animations from video. IEEE Trans. Vis. Comput. Graph., 11(5):540–549, 2005.

[43] J. P. Collomosse, G. McNeill, and Y. Qian. Storyboard sketches for content based video retrieval. In Proc. ICCV, 2009.

[44] J. P. Collomosse, D. Rowntree, and P.M. Hall. Stroke surfaces: Temporally coherent artistic animations from video. IEEE Trans. Vis. Comput. Graph., 11:540–549, 2005.

[45] D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24:603–619, 2002.

[46] D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603–619, 2002.

[47] T. T. A. Combs and B. B. Bederson. Does zooming improve image browsing? In Proc. DL, pages 130–137. ACM, 1999.

[48] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active shape modelstheir training and application. Comput. Vis. Image Underst., 61:38–59, January 1995.

[49] D. Cremers, F. R. Schmidt, and F. Barthel. Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions. In Proc. CVPR, 2008.

[50] C. Curtis, S. Anderson, J. Seims, K. Fleischer, and D. Salesin. Computer-generated watercolor. In Proc. SIGGRAPH, pages 421–430, 1997.

[51] D. DeCarlo and A. Santella. Stylization and abstraction of photographs. In Proc. SIGGRAPH, pages 769–776. ACM, 2002.

[52] Y. Delignon, A. Marzouki, and W. Pieczynski. Estimation of generalized mixtures and its application in image segmentation. IEEE Trans. Image Process., 6:1364– 1375, 1997.

[53] D. Dementhon. Spatio-temporal segmentation of video by hierarchical mean shift analysis. In Statistical Methods in Video Processing Workshop (SMVP), 2002.

[54] D. DeMenthon, V. Kobla, and D. Doermann. Video summarization by curve simplification. In Proc. MM, pages 211–218, 1998.

[55] Y. Deng, C. Kenney, M. Moore, and B. S. Manjunath. Peer group filtering and perceptual color image quantization. In Proc. ISCAS, pages 21–24, 1999.

[56] Y. Deng and B. S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell., 23(8):800–810, 2001.

[57] S. Dipaola. Painterly rendered portraits from photographs using a knowledgebased approach. In Proc. SPIE Human Vision and Imaging, 2007.

[58] P. Dollar. Supervised learning of edges and object boundaries. In Proc. CVPR, pages 1964–1971, 2006.

[59] R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. Wiley, 1973.

[60] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmen- tation. Int. J. Comput. Vision, 59(2):167–181, 2004.

[61] V. Ferrari, M. J. Mar ́ın-Jim ́enez, and A. Zisserman. Progressive search space reduction for human pose estimation. In Proc. CVPR, 2008.

[62] W. T. Freeman, J. B. Tenenbaum, and E. C. Pasztor. Learning style translation for the lines of a drawing. ACM Trans. Graph., 22(1):33–46, 2003.

[63] J. Freixenet, X. Mu ̃noz, D. Raba, J. Mart ́ı, and X. Cuf ́ı. Yet another survey on image segmentation: Region and boundary information integration. In Proc. ECCV, pages 408–422, 2002.

[64] B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972–976, 2007.

[65] K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1):32–40, 1975.

[66] A. Girgensohn, S. Bly, F. Shipman, J. Boreczky, and L. Wilcox. Home video editing made easy balancing automation and user control. In Proc. INTERACT, pages 464–471, 2001.

[67] A. Girgensohn, J. Boreczky, P. Chiu, J. Doherty, J. Foote, G. Golovchinsky, S. Uchihashi, and L. Wilcox. A semi-automatic approach to home video editing. In Proc. UIST, pages 81–89, New York, NY, USA, 2000. ACM.

[68] M. Gleicher. Image snapping. In Proc. SIGGRAPH, pages 183–190. ACM, 1995.

[69] D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.

[70] J. Goldberger, S. Gordon, and H.K. Greenspan. Unsupervised image-set clustering using an information theoretic framework. IEEE Trans. Image Process., 15:449– 458, 2006.

[71] D. B. Goldman, B. Curless, S. Seitz, and D. Salesin. Schematic storyboarding for video visualization and editing. In Proc. SIGGRAPH, pages 862–871, 2006.

[72] B. Gooch, G. Coombe, and P. Shirley. Artistic vision: Painterly rendering using computer vision techniques. In Proc. NPAR, pages 83–90, 2002.

[73] B. Gooch, E. Reinhard, and A. Gooch. Human facial illustrations: Creation and psychophysical evaluation. ACM Trans. Graph., 23(1):27–44, 2004.

[74] L. Grady. Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., pages 1768–1783, 2006.

[75] H. Greenspan, J. Goldberger, and A. Mayer. A probabilistic framework for spatio- temporal video representation and indexing. In Proc. ECCV, pages 461–475, 2002.

[76] M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph based video segmentation. Proc. CVPR, 2010.

[77] J.-Y. Guillemaut and A. Hilton. Joint multi-layer segmentation and reconstruction for free-viewpoint video applications. International Journal of Computer Vision, 93(1):73–100, 2011.

[78] V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman. Geodesic star convexity for interactive image segmentation. In Proc. CVPR, pages 3129–3136, 2010.

[79] P. Haeberli. Paint by numbers: Abstract image representations. In Proc. SIG- GRAPH, pages 207–214, 1990.

[80] P. Haggerty. Almost automatic computer painting. IEEE Comput. Graph. Appl., 11(6):11–12, 1991.

[81] P. M. Hall and Y. Hicks. CSBU-2004-03: A method to add gaussian mixture models. Technical report, Univ. Bath, 2004.

[82] J. Hays and I. Essa. Image and video based painterly animation. In Proc. NPAR, pages 113–120, 2004.

[83] J. Hays and I. Essa. Image and video based painterly animation. In Proc. NPAR, pages 113–120, 2004.

[84] X. He, R. S. Zemel, and D. Ray. Learning and incorporating top-down cues in image segmentation. In Proc. ECCV, pages 338–351, 2006.

[85] K. Heath, N. Gelfand, M. Ovsjanikov, M. Aanjaneya, and Guibas L. J. Image webs: Computing and exploiting connectivity in image collections. In Proc. CVPR, 2010.

[86] Paul Heckbert. Color image quantization for frame buffer display. Proc. SIG- GRAPH, pages 297–307, 1982.

[87] M. Heiler and C. Schnoerr. Natural image statistics for natural image segmentation. In Int. J. Comput. Vision, pages 1259–1266, 2003.

[88] A. Herbulot, S. Jehan-Besson, M. Barlaud, and G. Aubert. Shape gradient for image segmentation using information theory. In Proc. ICASSP, pages 17–21, 2004.

[89] A. Hertzmann. Painterly rendering with curved brush strokes of multiple sizes. In Proc. SIGGRAPH, pages 453–460, 1998.

[90] A. Hertzmann. Painterly rendering with curved brush strokes of multiple sizes. In Proc. SIGGRAPH, pages 453–460, 1998.

[91] A. Hertzmann. Fast paint texture. In Proc. NPAR, pages 91–96, 2002.

[92] A. Hertzmann. Tutorial: A survey of stroke-based rendering. IEEE Comput. Graph. Appl., 23(4):70–81, 2003.

[93] A. Hertzmann, C. Jacobs, N. Oliver, B. Curless, and D. Salesin. Image analogies. In Proc. SIGGRAPH, pages 327–340, 2001.

[94] A. Hertzmann and K. Perlin. Painterly rendering for video and interaction. In Proc. NPAR, pages 7–12, 2000.

[95] P. R. Hill, C. N. Canagarajah, and D. R. Bull. Image segmentation using a texture gradient based watershed transform. IEEE Trans. Image Process., 12(12):1618– 1633, 2003.

[96] D. Hoiem, A. A. Efros, and M. Hebert. Geometric context from a single image. In Proc. ICCV, pages 654–661. IEEE, October 2005.

[97] T. M. Hospedales and O. Williams. An adaptive machine director. In Proc. BMVC, 2008.

[98] R. Hu, T. Wang, and J. Collomosse. A bag-of-regions approach to sketch-based image retrieval. In Proc. ICIP, pages 3661–3664, 2011.

[99] X.-S. Hua, L. Lu, and H. Zhang. Optimization-based automated home video editing system. IEEE Trans. Circuits Syst. Video Techn., 14(5):572–583, 2004.

[100] M. Perdoch J. Cech, J. Matas. Efficient sequential correspondence selection by cosegmentation. In Proc. CVPR, 2008.

[101] M. Kagaya, W. Brendel, Q. Deng, T. Kesterson, S. Todorovic, P. J. Neill, and E. Zhang. Video painting with space-time-varying style parameters. IEEE Trans. Vis. Comput. Graph., 17(1):74–87, 2011.

[102] E. Kalogerakis, D. Nowrouzezahrai, S. Breslav, and A. Hertzmann. Learning Hatching for Pen-and-Ink Illustration of Surfaces. ACM Trans. Graph., 31(1), 2012. [103] H. Kang and Seungyong Lee. Shape-simplifying image abstraction. Computer Graphics Forum, 27(7):1773–1780, 2008.

[104] S. Khan and M. Shah. Object based segmentation of video using color, motion and spatial information. In Proc. CVPR, 2001.

[105] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi. Gradient flows and geometric active contour models. In Proc. ICCV, pages 810–815, 1995.

[106] J. Kim, J. W. Fisher, A. Yezzi, M. Cetin, and A. S. Willsky. A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. Image Process., 14:1486–1502, 2005.

[107] T. H. Kim, K. M. Lee, and S. U. Lee. Nonparametric higher-order learning for interactive segmentation. In Proc. CVPR, pages 3201–3208, 2010.

[108] P. Kohli, L. Ladicky, and P. H. S. Torr. Robust higher order potentials for enforcing label consistency. In Proc. CVPR, 2008.

[109] V. Kolmogorov and Y. Boykov. What metrics can be approximated by geo-cuts, or global optimization of length/area and flux. In Proc. ICCV, pages 564–571, 2005.

[110] V. Kolmogorov, Y. Boykov, and C. Rother. Applications of parametric maxflow in computer vision. In Proc. ICCV, pages 1–8, 2007.

[111] L. Kovar, M. Gleicher, and F. Pighin. Motion graphs. In Proc. SIGGRAPH, pages 473–482, Jul 2002.

[112] J. Koza and R. Poli. Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, 2005.

[113] J. R. Koza. Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Technical report, Stanford, CA, USA, 1990.

[114] S. Krishnamachari and M. Abdel-Mottaleb. Image browsing using hierarchical clustering. Computers and Communications, IEEE Symposium on, pages 301–307, 1999.

[115] J.-E. Kyprianidis. Image and video abstraction by multi-scale anisotropic Kuwa- hara filtering. In Proc. NPAR, pages 55–64, 2011.

[116] J.-E. Kyprianidis, J. Collomosse, T. Wang, and T. Isenberg. State of the art: A taxonomy of artistic stylization techniques for images and video. IEEE Trans. Vis. Comput. Graph., 2012.

[117] J.-E. Kyprianidis and J. Döllner. Image abstraction by structure adaptive filtering. In Proc. EG UK TPCG, pages 51–58, 2008.

[118] J.-E. Kyprianidis and J. Döllner. Image abstraction by structure adaptive filtering. In Proc. EG UK Theory and Practice of Computer Graphics, pages 51–58, 2008.

[119] J.-E. Kyprianidis and H. Kang. Image and video abstraction by coherence- enhancing filtering. Computer Graphics Forum, 30(2):593–602, 2011.

[120] J.-E. Kyprianidis, H. Kang, and J. D ̈ollner. Image and video abstraction by anisotropic Kuwahara filtering. Computer Graphics Forum, 28(7):1955–1963, 2009.

[121] J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML, pages 282–289, 2001.

[122] D. A. Langan, J. W. Modestino, and J. Zhang. Cluster validation for unsupervised stochastic model-based image segmentation. IEEE Trans. on Image Processing, 7(2):180–195, 1998.

[123] S. Lankton and A. Tannenbaum. Localizing region-based active contours. IEEE Trans. on Image Process., pages 2029–2039, 2008.

[124] H. Lee, S. Seo, S. Ryoo, and K. Yoon. Directional texture transfer. In Proc. NPAR, pages 43–50, 2010.

[125] Y. J. Lee and K. Grauman. Object-graphs for context-aware category discovery. pages 1–8, 2010.

[126] V. Lempitsky, P. Kohli, C. Rother, and T. Sharp. Image segmentation with a bounding box prior. Proc. ICCV, pages 277–284, 2009.

[127] V. Lempitsky, P. Kohli, C. Rother, and T. Sharp. Image segmentation with a bounding box prior. In Proc. ICCV, pages 277–284, 2009.

[128] T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vision, 43:29–44, June 2001.

[129] C. Li, C. Kao, J. C. Gore, and Z. Ding. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process., 17(10):1940–1949, October 2008.

[130] C. Li, C. Xu, C. Gui, and M. D. Fox. Level set evolution without re-initialization: A new variational formulation. In Proc. CVPR, pages 430–436. IEEE, 2005.

[131] W. Li, M. Agrawala, B. Curless, and D. Salesin. Automated generation of interactive 3D exploded view diagrams. ACM Trans. Graph., 27(3):101:1–7, 2008.

[132] Y. Li, J. Sun, and H.-Y. Shum. Video object cut and paste. In Proc. SIGGRAPH, pages 595–600, 2005.

[133] R. Lienhart. Abstracting home video automatically. In Proc. MM, 1999.

[134] L. Lin, K. Zeng, H. Lv, Y. Wang, Y. Xu, and S.-C. Zhu. Painterly animation using video semantics and feature correspondence. In Proc. NPAR, pages 73–80, 2010.

[135] T. Lin. Automatic video scene extraction by shot grouping. In Proc. ICPR, pages 39–42, 2000.

[136] P. Litwinowicz. Processing images and video for an impressionist effect. In Proc. SIGGRAPH, pages 407–414, 1997.

[137] C. Liu, J. Yuen, and A. Torralba. Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell., 33(5):978–994, 2011.

[138] C. Liu, J. Yuen, A. B. Torralba, J. Sivic, and W. T. Freeman. Sift flow: Dense correspondence across different scenes. In Proc. ECCV, pages 28–42, 2008.

[139] H. Lombaert, Y. Sun, L. Grady, and C. Xu. A multilevel banded graph cuts method for fast image segmentation. In Proc. ICCV, pages 259–265, 2005.

[140] D. G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, pages 1150–1157, Washington, DC, USA, 1999. IEEE.

[141] D.G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60:91–110, 2004.

[142] Y.-F. Ma, L. Lu, H.-J. Zhang, and M. Li. A user attention model for video summarization. In Proc. MM, pages 533–542. ACM, 2002.

[143] J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. Int. J. Comput. Vision, 43(1):7–27, 2001.

[144] D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. ICCV, pages 416–423, 2001.

[145] D. R. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell., 26(5):530–549, 2004.

[146] E. McKone, N. Kanwisher, and C. Duchaine. Can generic expertise explain special processing for faces? Trends in Cognitive Science, 11:8–15, 2007.

[147] P. Mehrani and O. Veksler. Saliency segmentation based on learning and graph cut refinement. In Proc. BMVC, pages 110.1–12, 2010.

[148] T. Mei, X.-S. Hua, H.-Q. Zhou, and S. Li. Modeling and mining of users capture intention for home videos. IEEE Trans. MM, 9, 2007.

[149] B. J. Meier. Painterly rendering for animation. In Proc. ACM SIGRGAPH, pages 477–484, 1996.

[150] M. Meng, M. Zhao, and S.-C. Zhu. Artistic paper-cut of human portraits. In Proc. MM, pages 931–934, 2010.

[151] F. Moscheni, S. Bhattacharjee, and M. Kunt. Spatiotemporal segmentation based on region merging. IEEE Trans. Pattern Anal. Mach. Intell., 20:897–915, 1998.

[152] D. Mumford and J. Shah. Boundary detection by minimizing functionals. In Proc. CVPR, pages 22–26. IEEE, 1985.

[153] A. Nagasaka and Y. Tanaka. Automatic video indexing and full-video search for object appearances. In Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II, pages 113–127, 1992.

[154] R. Oami, A. B. Benitez, S.-F. Chang, and N. Dimitrova. Understanding and modeling user interests in consumer videos. In Proc. ICME, pages 1475–1478, 2004.

[155] M. Obaid, R. Mukundan, and M. Billinghurst. Rendering and animating expressive caricatures. In Proc. ICCSIT, pages 401–406, 2010.

[156] P. O’Donovan and A. Hertzmann. AniPaint: Interactive painterly animation from video. IEEE Trans. Vis. Comput. Graph., 18(3):475–487, 2012.

[157] N. R. Pal and S. K. Pal. A review on image segmentation techniques. Pattern Recognition, 26(9):1277–1294, 1993.

[158] D. K. Panjwani and G. Healey. Markov random field models for unsupervised segmentation of textured color images. IEEE Trans. Pattern Anal. Mach. Intell., 17(10):939–954, 1995.

[159] N. Paragios and R. Deriche. A pde-based level-set approach for detection and tracking of moving objects. In Proc. ICCV, pages 1139–1145, 1998.

[160] N. Paragios and R. Deriche. Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, pages 249–268, 2002.

[161] S. Paris. Edge-preserving smoothing and mean-shift segmentation of video streams. In Proc. ECCV, pages 460–473, 2008.

[162] S. Paris and F. Durand. A topological approach to hierarchical segmentation using mean shift. In Proc. CVPR, pages 1–8, 2007.

[163] I. Patras, E. A. Hendriks, and R. L. Lagendijk. Video segmentation by map labeling of watershed segments. IEEE Trans. Pattern Anal. Mach. Intell., 32(9):1553–1567, 2001.

[164] P. Perez, M. Gangnet, and A. Blake. Poisson image editing. In SIGGRAPH, pages 313–318, 2003.

[165] R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via and freely available at, 2008.

[166] T. Pouli and E. Reinhard. Progressive color transfer for images of arbitrary dynamic range. Computers & Graphics, 35(1):67–80, 2011.

[167] B. Price, B. Morse, and S. Cohen. Learning-based interactive video segmentation by evaluation of multiple propogated cues. In Proc. ICCV, 2009.

[168] B. L. Price and S. Cohen. Stereocut: Consistent interactive object selection in stereo image pairs. In Proc. ICCV, 2011.

[169] B. L. Price, B. S. Morse, and S. Cohen. Geodesic graph cut for interactive image segmentation. In Proc. CVPR, pages 3161–3168, 2010.

[170] C. Primo, A. Hernandez, and S. Escalera. Automatic user interaction correciton via multi-label graph cuts. In Proc. ICCV Workshop on HCI in Computer Vision, 2011.

[171] A. Protiere and G. Sapiro. Interactive image segmentation via adaptive weighted distances. IEEE Trans. Image Process., 16, 2007.

[172] A. Rabinovich, S. Belongie, T. Lange, and J. M. Buhmann. Model order selection and cue combination for image segmentation. In Proc. CVPR, pages 1130–1137, 2006.

[173] E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. Colour transfer between images. IEEE Comput. Graph. Appl., 21:34–41, 2001.

[174] X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. ICCV, volume 1, pages 10–17, 2003.

[175] M. Ristivojevic and J. Konrad. Space-time image sequence analysis: object tunnels and occlusion volumes. IEEE Trans. Image Processing, 15(2):364–376, 2006.

[176] K. Rodden, W. Basalaj, D. Sinclair, and K. Wood. Does organisation by similarity assist image browsing? In Proc. CHI, pages 190–197. ACM, 2001.

[177] P. L. Rosin and Y.-K. Lai. Towards artistic minimal rendering. In Proc. NPAR, pages 119–127, 2010.

[178] C. Rother, V. Kolmogorov, and A. Blake. Grabcut - interactive foreground extraction using iterated graph cuts. In Proc. SIGGRAPH. ACM, 2004.

[179] C. Rother, V. Kolmogorov, Y. Boykov, and A. Blake. Interactive foreground ex- traction using graph cut. Technical Report MSR-TR-2011-46, Microsoft Research, UK, 2011.

[180] M. Rousson and N. Paragios. Shape priors for level set representations. In Proc. ECCV, pages 78–92. Springer, 2002.

[181] B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In Proc. CVPR, pages 1605–1614, 2006.

[182] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: A database and web-based tool for image annotation. Int. J. Comput. Vision, 77:157–173, May 2008.

[183] A. Santella and D. DeCarlo. Abstracted painterly renderings using eye-tracking data. In Proc. NPAR, pages 75–82, 2002.

[184] G. Schaefer. A next generation browsing environment for large image repositories. Multimedia Tools Appl., 47(1):105–120, 2010.

[185] A. Schodl, R. Skeliski, D. Salesin, and H. Essa. Video textures. In Proc. SIG- GRAPH, pages 489–498, Jul 2000.

[186] L. Shafarenko, M. Petrou, and J. Kittler. IEEE Trans. on Image Processing, 6(11):1530–1544, 1997.

[187] J. Shi and J. Malik. Motion segmentation and tracking using normalized cuts. In Proc. ICCV, pages 1154–1160, 1998.

[188] J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888–905, 2000.

[189] M. Shiraishi and Y. Yamaguchi. An algorithm for automatic painterly rendering based on local source image approximation. In Proc. NPAR, pages 53–58, 2000.

[190] J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost for image under- standing: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vision, 81:2–23, January 2009.

[191] M. Shugrina, M. Betke, and J. Collomosse. Empathic painting: Interactive stylization using observed emotional state. In Proc. NPAR, pages 87–96, 2006.

[192] B. Sigurbj ̈ornsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In WWW 2008, pages 327–336, 2008.

[193] D. Simakov, Y. Caspi, E. Shechtman, and M. Irani. Summarizing visual data using bidirectional similarity. In Proc. CVPR, pages 119–127, 2008.

[194] D. Singaraju, L. Grady, and R. Vidal. P-brush: Continuous valued mrfs with normed pairwise distributions for image segmentation. In Proc. CVPR, pages 1303–1310, 2009.

[195] P. Sinha. Face recognition by humans: Nineteen results all computer vision researchers should know about. In Proceedings of the IEEE, pages 1948–1962, 2006.

[196] A. K. Sinop and L. Grady. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In Proc. ICCV, pages 1–8, 2007.

[197] J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV, volume 2, pages 1470–1477, 2003.

[198] Y. Song, P. M. Hall, P. Rosin, and J. Collomosse. Arty shapes. In Proc. CAe, pages 65–72, 2008.

[199] A. N. Stein and M. Hebert. Occlusion boundaries from motion: Low-level detection and mid-level reasoning. Int. J. Comput. Vision, 82(3):325–357, 2009.

[200] S. Strassmann. Hairy brushes. In Proc. SIGGRAPH, pages 225–232, 1986.

[201] M. F. Tappen and W. T. Freeman. Comparison of graph cuts with belief propa- gation for stereo, using identical mrf parameters. In Proc. ICCV, pages 900–907, 2003.

[202] D. A. Tolliver and G. L. Miller. Graph partitioning by spectral rounding: Appli- cations in image segmentation and clustering. In Proc. CVPR, pages 1053–1060, 2006.

[203] M. Tominaga, S. Fukuoka, K. Murakami, and H. Koshimizu. Facial caricaturing with motion caricaturing in PICASSO system. In Proc. ICAIM, page 30, 1997.

[204] M. Tominaga, J.-I. Hayashi, K Murakami, and H. Koshimizu. Facial caricaturing system PICASSO with emotional motion deformation. In Proc. KES, pages 205–214, 1998.

[205] S. M. F. Treavett and M. Chen. Statistical techniques for the automated synthesis of non-photorealistic images. In Proc. EGUK, pages 201–210, 1997.

[206] D. Tsai, M. Flagg, and J. Rehg. Motion coherent tracking with multi-label mrf optimization. In Proc. BMVC, pages 56.1–11, 2010.

[207] K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582–1596, 2010.

[208] M. Varma and A. Zisserman. A statistical approach to texture classification from single images. Int. J. Comput. Vision, 62:61–81, 2005.

[209] P. Viola and M. J. Jones. Robust real-time face detection. Int. J. Comput. Vision, 57(2):137–154, May 2004.

[210] J. Wang, M. Agrawala, and M. F. Cohen. Soft scissors: an interactive tool for realtime high quality matting. In Proc. SIGGRAPH, pages 585–594. ACM, 2007.

[211] J. Wang, P. Bhat, A. Colburn, M. Agrawala, and M. F. Cohen. Interactive video cutout. In Proc. SIGGRAPH, pages 585–594, 2005.

[212] J. Wang, B. Thiesson, Y. Xu, and M. Cohen. Image and video segmentation by anisotropic kernel mean shift. In Proc. ECCV, pages 238–249. Springer, 2004.

[213] J. Wang, Y. Xu, H. Shum, and M. Cohen. Video tooning. In Proc. SIGGRAPH, volume 23, pages 574–583, 2004.

[214] J.-P. Wang. Stochastic relaxation on partitions with connected components and its application to image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 20(6):619–636, 1998.

[215] L. Wang, B. Zeng, S. Lin, G. Xu, and H.-Y. Shum. Automatic extraction of semantic colors in sports video. In Proc. ICASSP, pages 617–620, 2004.

[216] T. Wang and J. Collomosse. Progressive motion diffusion of labeling priors for coherent video segmentation. IEEE Transactions on Multimedia, 14(2):389–400, April 2012.

[217] T. Wang, J. Collomosse, A. Hunter, and D. Greig. Digital raphael: Learnable stroke models for example-based portrait painting. submitted to IEEE Trans. Vis. Comput. Graph., 2012.

[218] T. Wang, J. P. Collomosse, R. Hu, D. Slatter, D. Greig, and P. Cheatle. Stylized ambient displays of digital media collections. Computers & Graphics, 35(1):54–66, 2011.

[219] T. Wang, J. P. Collomosse, D. Slatter, P. Cheatle, and D. Greig. Video stylization for digital ambient displays of home movies. In Proc. NPAR, pages 137–146, 2010.

[220] T. Wang, J.-Y. Guillemaut, and J. P. Collomosse. Multi-label propagation for coherent video segmentation and artistic stylization. In Proc. ICIP, pages 3005– 3008, 2010.

[221] T. Wang, B. Han, and J. Collomosse. Touchcut: Fast image and video segmen- tation using single-touch interaction. submitted to Computer Vision and Image Understanding (CVIU), 2012.

[222] T. Wang, B. Han, and J. Collomosse. Touchcut: Single-touch object segmentation driven by level set methods. In Proc. ICASSP, 2012.

[223] T. Wang, A. Mansfield, R. Hu, and J. Collomosse. An evolutionary approach to automatic video editing. In Proc. CVMP, Nov 2009.

[224] X. Wang and X. Tang. Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell., 31(11):1955–1967, 2009.

[225] J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. pages 1800–1807, 2005.

[226] H. Winnemoller, S.C. Olsen, and B. Gooch. Real-time video abstraction. In Proc. SIGGRAPH, pages 1221–1226, 2006.

[227] W. You, S. Feis, and R. Lea. Studying vision-based multiple-user interaction with in-home large displays. In Proc. 3rd ACM workshop on Human-Centred Computing (HCC), pages 19–26, 2008.

[228] K. Zeng, M. Zhao, C. Xiong, and S.-C. Zhu. From image parsing to painterly rendering. ACM Trans. Graph., 29(1):2:1–11, 2009.

[229] W. Zhang, X. Wang, and X. Tang. Lighting and pose robust face sketch synthesis. In Proc. ECCV, pages 420–433, 2010.

[230] H. Zhao, T. Chan, B. Merriman, and S. Osher. A variational level set approach to multiphase motion. Journal of Computational Physics, pages 179–195, 1996.

[231] M. Zhao and S.-C. Zhu. Sisley the abstract painter. In Proc. NPAR, pages 99–107, 2010.

[232] M. Zhao and S.-C. Zhu. Portrait painting using active templates. In Proc. NPAR, pages 117–124, 2011.

[233] S. Zhu and A. Yuille. Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., pages 884–900, 1996.


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