The Visual Computer

, Volume 29, Issue 5, pp 393–405 | Cite as

Internet visual media processing: a survey with graphics and vision applications

  • Shi-Min Hu
  • Tao Chen
  • Kun Xu
  • Ming-Ming Cheng
  • Ralph R. Martin
Original Article


In recent years, the computer graphics and computer vision communities have devoted significant attention to research based on Internet visual media resources. The huge number of images and videos continually being uploaded by millions of people have stimulated a variety of visual media creation and editing applications, while also posing serious challenges of retrieval, organization, and utilization. This article surveys recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis. It discusses the problems involved, and suggests possible future directions in this emerging research area.


Internet visual media Large databases Images Video Survey 



This work was supported by the National Basic Research Project of China (Project Number 2011CB302205), the Natural Science Foundation of China (Project Number 61120106007 and 61103079), and the National High Technology Research and Development Program of China (Project Number 2012AA011802), and an EPSRC Travel Grant.


  1. 1.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of IEEE CVPR, pp. 1597–1604 (2009) Google Scholar
  2. 2.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a day. In: Proceedings of IEEE ICCV, pp. 72–79 (2009) Google Scholar
  3. 3.
    An, X., Pellacini, F.: Appprop: all-pairs appearance-space edit propagation. ACM Trans. Graph. 27(3), 40:1–40:9 (2008) CrossRefGoogle Scholar
  4. 4.
    Bagon, S., Boiman, O., Irani, M.: What is a good image segment? A unified approach to segment extraction. In: Proceedings of ECCV, pp. 30–44. Springer, Berlin (2008) Google Scholar
  5. 5.
    Bai, X., Li, Q., Latecki, L., Liu, W., Tu, Z.: Shape band: a deformable object detection approach. In: Proceedings of IEEE CVPR, pp. 1335–1342 (2009) Google Scholar
  6. 6.
    Bai, X., Yang, X., Latecki, L.J., Liu, W., Tu, Z.: Learning context-sensitive shape similarity by graph transduction. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 861–874 (2010) CrossRefGoogle Scholar
  7. 7.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002) CrossRefGoogle Scholar
  8. 8.
    Berg, T., Berg, A.: Finding iconic images. In: Proceedings of IEEE CVPR, pp. 1–8 (2009) Google Scholar
  9. 9.
    Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., Nayar, S.K.: Face swapping: automatically replacing faces in photographs. ACM Trans. Graph. 27(3), 39:1–39:8 (2008) CrossRefGoogle Scholar
  10. 10.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006) CrossRefGoogle Scholar
  11. 11.
    Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., Zhang, L.: Mindfinder: interactive sketch-based image search on millions of images. In: Proceedings of MM, pp. 1605–1608. ACM, New York (2010) Google Scholar
  12. 12.
    Chen, J., Paris, S., Durand, F.: Real-time edge-aware image processing with the bilateral grid. ACM Trans. Graph. 26(3), 103:1–103:9 (2007) CrossRefGoogle Scholar
  13. 13.
    Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2photo: Internet image montage. ACM Trans. Graph. 28(5), 124:1–124:10 (2009) Google Scholar
  14. 14.
    Chen, T., Tan, P., Ma, L.Q., Cheng, M.M., Shamir, A., Hu, S.M.: Poseshop: human image database construction and personalized content synthesis. IEEE Trans. Vis. Comput. Graph. 19 (2013).
  15. 15.
    Cheng, M.M., Zhang, G.X.: Connectedness of random walk segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 200–202 (2011) CrossRefGoogle Scholar
  16. 16.
    Cheng, M.M., Zhang, F.L., Mitra, N.J., Huang, X., Hu, S.M.: Repfinder: finding approximately repeated scene elements for image editing. ACM Trans. Graph. 29, 83:1–83:8 (2008) Google Scholar
  17. 17.
    Cheng, M.M., Zhang, G.X., Mitra, N., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: Proceedings of IEEE CVPR, pp. 409–416 (2011) Google Scholar
  18. 18.
    Chia, A.Y.S., Zhuo, S., Gupta, R.K., Tai, Y.W., Cho, S.Y., Tan, P., Lin, S.: Semantic colorization with Internet images. ACM Trans. Graph. 30(6), 156 (2011) CrossRefGoogle Scholar
  19. 19.
    Christopoulos, C., Skodras, A., Ebrahimi, T.: The jpeg2000 still image coding system: an overview. IEEE Trans. Consum. Electron. 46(4), 1103–1127 (2000) CrossRefGoogle Scholar
  20. 20.
    Cong, L., Tong, R., Dong, J.: Selective image abstraction. Vis. Comput. 27(3), 187–198 (2011) CrossRefGoogle Scholar
  21. 21.
    Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: Proceedings of WWW, pp. 761–770. ACM, New York (2009) CrossRefGoogle Scholar
  22. 22.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE CVPR, vol. 1, pp. 886–893 (2005) Google Scholar
  23. 23.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 5:1–50:60 (2008) CrossRefGoogle Scholar
  24. 24.
    Del Bimbo, A., Pala, P.: Visual image retrieval by elastic matching of user sketches. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 121–132 (1997) CrossRefGoogle Scholar
  25. 25.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18(1), 193–222 (1995) CrossRefGoogle Scholar
  26. 26.
    Diakopoulos, N., Essa, I., Jain, R.: Content based image synthesis. In: Proceedings of CIVR, pp. 299–307 (2004) Google Scholar
  27. 27.
    Ding, M., Tong, R.F.: Content-aware copying and pasting in images. Vis. Comput. 26(6–8), 721–729 (2010) CrossRefGoogle Scholar
  28. 28.
    Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes Paris look like Paris? ACM Trans. Graph. 31(4), 101:1–101:9 (2012) CrossRefGoogle Scholar
  29. 29.
    Eitz, M., Hildebrand, K., Boubekeur, T., Alexa, M.: Sketch-based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans. Vis. Comput. Graph. 17(11), 1624–1636 (2011) CrossRefGoogle Scholar
  30. 30.
    Eitz, M., Richter, R., Hildebrand, K., Boubekeur, T., Alexa, M.: Photosketcher: interactive sketch-based image synthesis. IEEE Comput. Graph. Appl. 31(6), 56–66 (2011) CrossRefGoogle Scholar
  31. 31.
    Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2179–2195 (2009) CrossRefGoogle Scholar
  32. 32.
    Everingham, M., Zisserman, A., Williams, C.K.I., van Gool, L.: The PASCAL Visual Object Classes Challenge 2006 (VOC2006) Results (2006).
  33. 33.
    Farbman, Z., Hoffer, G., Lipman, Y., Cohen-Or, D., Lischinski, D.: Coordinates for instant image cloning. ACM Trans. Graph. 28(3), 67:1–67:9 (2009) CrossRefGoogle Scholar
  34. 34.
    Fattal, R., Carroll, R., Agrawala, M.: Edge-based image coarsening. ACM Trans. Graph. 29(1), 6 (2009) CrossRefGoogle Scholar
  35. 35.
    Feng, B., Cao, J., Bao, X., Bao, L., Zhang, Y., Lin, S., Yun, X.: Graph-based multi-space semantic correlation propagation for video retrieval. Vis. Comput. 27(1), 21–34 (2011) CrossRefGoogle Scholar
  36. 36.
    Fiss, J., Agarwala, A., Curless, B.: Candid portrait selection from video. ACM Trans. Graph. 30(6), 128:1–128:8 (2011) CrossRefGoogle Scholar
  37. 37.
    Frahm, J.M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a cloudless day. In: Proceedings of ECCV, pp. 368–381. Springer, Berlin (2010) Google Scholar
  38. 38.
    Furukawa, Y., Curless, B., Seitz, S., Szeliski, R.: Towards Internet-scale multi-view stereo. In: Proceedings of IEEE CVPR, pp. 1434–1441 (2010) Google Scholar
  39. 39.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001) CrossRefGoogle Scholar
  40. 40.
    Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.: Multi-view stereo for community photo collections. In: Proceedings of IEEE ICCV, pp. 1–8 (2007) Google Scholar
  41. 41.
    Goldberg, C., Chen, T., Zhang, F.L., Shamir, A., Hu, S.M.: Data-driven object manipulation in images. Comput. Graph. Forum 31(2pt1), 265–274 (2012) CrossRefGoogle Scholar
  42. 42.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006) CrossRefGoogle Scholar
  43. 43.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical report (2007) Google Scholar
  44. 44.
    Haber, T., Fuchs, C., Bekaer, P., Seidel, H.P., Goesele, M., Lensch, H.: Relighting objects from image collections. In: Proceedings of IEEE CVPR, pp. 627–634 (2009) Google Scholar
  45. 45.
    Han, D., Sonka, M., Bayouth, J.E., Wu, X.: Optimal multiple-seams search for image resizing with smoothness and shape prior. Vis. Comput. 26(6–8), 749–759 (2010) CrossRefGoogle Scholar
  46. 46.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of NIPS, Cambridge, MA, pp. 545–552 (2006) Google Scholar
  47. 47.
    Hata, M., Toyoura, M., Mao, X.: Automatic generation of accentuated pencil drawing with saliency map and LIC. Vis. Comput. 28(6–8), 657–668 (2012) CrossRefGoogle Scholar
  48. 48.
    Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Trans. Graph. 26(3), 4:1–4:7 (2007) CrossRefGoogle Scholar
  49. 49.
    He, J., Feng, J., Liu, X., Cheng, T., Lin, T.H., Chung, H., Chang, S.F.: Mobile product search with bag of hash bits and boundary reranking. In: Proceedings of IEEE CVPR, pp. 3005–3012 (2012) Google Scholar
  50. 50.
    Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., Guibas, L.: Image webs: computing and exploiting connectivity in image collections. In: Proceedings of IEEE CVPR, pp. 3432–3439 (2010) Google Scholar
  51. 51.
    Hirata, K., Kato, T.: Query by visual example - content based image retrieval. In: Proceedings of EDBT’ 92, pp. 56–71. Springer, London (1992) Google Scholar
  52. 52.
    Huang, H., Xiao, X.: Example-based contrast enhancement by gradient mapping. Vis. Comput. 26(6–8), 731–738 (2010) CrossRefGoogle Scholar
  53. 53.
    Huang, H., Zang, Y., Li, C.F.: Example-based painting guided by color features. Vis. Comput. 26(6–8), 933–942 (2010) CrossRefGoogle Scholar
  54. 54.
    Huang, H., Fu, T.N., Li, C.F.: Painterly rendering with content-dependent natural paint strokes. Vis. Comput. 27(9), 861–871 (2011) CrossRefGoogle Scholar
  55. 55.
    Huang, H., Zhang, L., Zhang, H.C.: Arcimboldo-like collage using Internet images. ACM Trans. Graph. 30(6), 155:1–155:8 (2011) Google Scholar
  56. 56.
    Huang, M.C., Liu, F., Wu, E.: A gpu-based matting Laplacian solver for high resolution image matting. Vis. Comput. 26(6–8), 943–950 (2010) CrossRefGoogle Scholar
  57. 57.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998) CrossRefGoogle Scholar
  58. 58.
    Jin, Y., Liu, L., Wu, Q.: Nonhomogeneous scaling optimization for realtime image resizing. Vis. Comput. 26(6–8), 769–778 (2010) CrossRefGoogle Scholar
  59. 59.
    Jing, Y., Baluja, S.: Visualrank: applying pagerank to large-scale image search. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1877–1890 (2008) CrossRefGoogle Scholar
  60. 60.
    Johnson, M., Brostow, G.J., Shotton, J., Arandjelović, O., Kwatra, V., Cipolla, R.: Semantic photo synthesis. Comput. Graph. Forum 25(3), 407–413 (2006) CrossRefGoogle Scholar
  61. 61.
    Johnson, M.K., Dale, K., Avidan, S., Pfister, H., Freeman, W.T., Matusik, W.: Cg2real: improving the realism of computer generated images using a large collection of photographs. IEEE Trans. Vis. Comput. Graph. 17(9), 1273–1285 (2011) CrossRefGoogle Scholar
  62. 62.
    Kalogerakis, E., Vesselova, O., Hays, J., Efros, A., Hertzmann, A.: Image sequence geolocation with human travel priors. In: Proceedings of IEEE ICCV, pp. 253–260 (2009) Google Scholar
  63. 63.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988) CrossRefGoogle Scholar
  64. 64.
    Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: Proceedings of MM, pp. 631–640. ACM, New York (2007) Google Scholar
  65. 65.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985) Google Scholar
  66. 66.
    Kuthirummal, S., Agarwala, A., Goldman, D.B., Nayar, S.K.: Priors for large photo collections and what they reveal about cameras. In: Proceedings of ECCV, pp. 74–87. Springer, Berlin (2008) Google Scholar
  67. 67.
    Lalonde, J.F., Hoiem, D., Efros, A.A., Rother, C., Winn, J., Criminisi, A.: Photo clip art. ACM Trans. Graph. 26(3), 3:1–3:10 (2007) CrossRefGoogle Scholar
  68. 68.
    Lee, Y.J., Zitnick, C.L., Cohen, M.F.: Shadowdraw: real-time user guidance for freehand drawing. ACM Trans. Graph. 30, 27:1–27:10 (2011) CrossRefGoogle Scholar
  69. 69.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. 23(3), 689–694 (2004) CrossRefGoogle Scholar
  70. 70.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008) CrossRefGoogle Scholar
  71. 71.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimed. Comput. Commun. Appl. 2(1), 1–19 (2006) CrossRefGoogle Scholar
  72. 72.
    Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.M.: Modeling and recognition of landmark image collections using iconic scene graphs. In: Proceedings of ECCV, pp. 427–440. Springer, Berlin (2008) Google Scholar
  73. 73.
    Li, Y., Crandall, D., Huttenlocher, D.: Landmark classification in large-scale image collections. In: Proceedings of IEEE ICCV, pp. 1957–1964 (2009) Google Scholar
  74. 74.
    Li, Y., Ju, T., Hu, S.M.: Instant propagation of sparse edits on images and videos. Comput. Graph. Forum 29(7), 2049–2054 (2010) CrossRefGoogle Scholar
  75. 75.
    Ling, Y., Yan, C., Liu, C., Wang, X., Li, H.: Adaptive tone-preserved image detail enhancement. Vis. Comput. 28(6–8), 733–742 (2012) CrossRefGoogle Scholar
  76. 76.
    Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. 25(3), 646–653 (2006) CrossRefGoogle Scholar
  77. 77.
    Liu, H., Zhang, L., Huang, H.: Web-image driven best views of 3d shapes. Vis. Comput. 28(3), 279–287 (2012) MathSciNetCrossRefGoogle Scholar
  78. 78.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011) CrossRefGoogle Scholar
  79. 79.
    Liu, X., Wan, L., Qu, Y., Wong, T.T., Lin, S., Leung, C.S., Heng, P.A.: Intrinsic colorization. ACM Trans. Graph. 27(5), 152:1–152:9 (2008) CrossRefGoogle Scholar
  80. 80.
    Lu, S.P., Zhang, S.H., Wei, J., Hu, S.M., Martin, R.R.: Time-line editing of objects in video. IEEE Trans. Vis. Comput. Graph. 19 (2013).
  81. 81.
    Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of MM, pp. 374–381. ACM, New York (2003) Google Scholar
  82. 82.
    Mortensen, E.N., Barrett, W.A.: Intelligent scissors for image composition. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH ’95, pp. 191–198. ACM, New York (1995) CrossRefGoogle Scholar
  83. 83.
    Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: Proceedings of IEEE CVPR, vol. 2, pp. 2049–2056 (2006) Google Scholar
  84. 84.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001) MATHCrossRefGoogle Scholar
  85. 85.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999) Google Scholar
  86. 86.
    Pajak, D., Cadík, M., Aydin, T.O., Okabe, M., Myszkowski, K., Seidel, H.P.: Contrast prescription for multiscale image editing. Vis. Comput. 26(6–8), 739–748 (2010) CrossRefGoogle Scholar
  87. 87.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. 22(3), 313–318 (2003) CrossRefGoogle Scholar
  88. 88.
    Quack, T., Leibe, B., Van Gool, L.: World-scale mining of objects and events from community photo collections. In: Proceedings of CIVR, pp. 47–56. ACM, New York (2008) CrossRefGoogle Scholar
  89. 89.
    Rother, C., Kolmogorov, V., Blake, A.: “Grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004) CrossRefGoogle Scholar
  90. 90.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008) CrossRefGoogle Scholar
  91. 91.
    Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: Proceedings of IEEE CVPR, vol. 2, pp. 37–44 (2004) Google Scholar
  92. 92.
    Salembier, P., Sikora, T., Manjunath, B.: Introduction to MPEG-7: Multimedia Content Description Interface. Wiley, New York (2002) Google Scholar
  93. 93.
    Sethian, J.: Level set methods and fast marching methods: evolving interfaces. In: Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, vol. 3. Cambridge University Press, Cambridge (1999) Google Scholar
  94. 94.
    Shen, L., Tan, P.: Photometric stereo and weather estimation using Internet images. In: Proceedings of IEEE CVPR, pp. 1850–1857 (2009) Google Scholar
  95. 95.
    Shrivastava, A., Malisiewicz, T., Gupta, A., Efros, A.A.: Data-driven visual similarity for cross-domain image matching. ACM Trans. Graph. 30(6), 154:1–154:10 (2011) CrossRefGoogle Scholar
  96. 96.
    Sigal, L., Black, M.: Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion. Brown University Technical Report 120 (2006) Google Scholar
  97. 97.
    Simon, I., Snavely, N., Seitz, S.: Scene summarization for online image collections. In: Proceedings of IEEE ICCV, pp. 1–8 (2007) Google Scholar
  98. 98.
    Sinop, A., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proceedings of IEEE ICCV, pp. 1–8 (2007) Google Scholar
  99. 99.
    Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of IEEE ICCV, vol. 2, pp. 1470–1477 (2003) Google Scholar
  100. 100.
    Snavely, N.: Scene reconstruction and visualization from Internet photo collections: a survey. IPSJ Trans. Comput. Vis. Appl. 3(0), 44–66 (2011) Google Scholar
  101. 101.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25(3), 835–846 (2006) CrossRefGoogle Scholar
  102. 102.
    Snavely, N., Garg, R., Seitz, S.M., Szeliski, R.: Finding paths through the world’s photos. ACM Trans. Graph. 27(3), 15:1–15:11 (2008) CrossRefGoogle Scholar
  103. 103.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from Internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008) CrossRefGoogle Scholar
  104. 104.
    Sorokin, A., Forsyth, D.: Utility data annotation with amazon mechanical turk. In: Proceedings of IEEE CVPR, pp. 1–8 (2008) Google Scholar
  105. 105.
    Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Trans. Graph. 23(3), 315–321 (2004) CrossRefGoogle Scholar
  106. 106.
    Tao, L., Yuan, L., Sun, J.: Skyfinder: attribute-based sky image search. ACM Trans. Graph. 28(3), 68:1–68:5 (2009) CrossRefGoogle Scholar
  107. 107.
    Thayananthan, A., Stenger, B., Torr, P., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: Proceedings of IEEE CVPR, vol. 1, pp. 127–133 (2003) Google Scholar
  108. 108.
    Tompkin, J., Kim, K.I., Kautz, J., Theobalt, C.: Videoscapes: exploring sparse, unstructured video collections. ACM Trans. Graph. 31(5), 68:1–68:12 (2012) Google Scholar
  109. 109.
    Treisman, A., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980) CrossRefGoogle Scholar
  110. 110.
    Tsotsos, J.: Analyzing vision at the complexity level. Behav. Brain Sci. 13(3), 423–469 (1990) CrossRefGoogle Scholar
  111. 111.
    Wang, B., Yu, Y., Wong, T.T., Chen, C., Xu, Y.Q.: Data-driven image color theme enhancement. ACM Trans. Graph. 29(6), 146:1–146:10 (2010) Google Scholar
  112. 112.
    Wang, D., Li, G., Jia, W., Luo, X.: Saliency-driven scaling optimization for image retargeting. Vis. Comput. 27(9), 853–860 (2011) CrossRefGoogle Scholar
  113. 113.
    Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. 21(3), 277–280 (2002) CrossRefGoogle Scholar
  114. 114.
    Wu, J., Shen, X., Liu, L.: Interactive two-scale color-to-gray. Vis. Comput. 28(6–8), 723–731 (2012) MATHCrossRefGoogle Scholar
  115. 115.
    Xiao, C., Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28(6–8), 713–721 (2012) CrossRefGoogle Scholar
  116. 116.
    Xie, Z.F., Shen, Y., Ma, L.Z., Chen, Z.H.: Seamless video composition using optimized mean-value cloning. Vis. Comput. 26(6–8), 1123–1134 (2010) CrossRefGoogle Scholar
  117. 117.
    Xie, Z.F., Lau, R.W., Gui, Y., Chen, M.G., Ma, L.Z.: A gradient-domain-based edge-preserving sharpen filter. Vis. Comput. 28(12), 1195–1207 (2012) CrossRefGoogle Scholar
  118. 118.
    Xu, K., Li, Y., Ju, T., Hu, S.M., Liu, T.Q.: Efficient affinity-based edit propagation using k-d tree. ACM Trans. Graph. 28(5), 118:1–118:6 (2009) Google Scholar
  119. 119.
    Xue, S., Agarwala, A., Dorsey, J., Rushmeier, H.: Understanding and improving the realism of image composites. ACM Trans. Graph. 31(4), 84:1–84:10 (2012) CrossRefGoogle Scholar
  120. 120.
    Yang, X., Koknar-Tezel, S., Latecki, L.: Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In: Proceedings of IEEE CVPR, pp. 357–364 (2009) Google Scholar
  121. 121.
    Zhang, F.L., Cheng, M.M., Jia, J., Hu, S.M.: Imageadmixture: putting together dissimilar objects from groups. IEEE Trans. Vis. Comput. Graph. 18(11), 1849–1857 (2012) CrossRefGoogle Scholar
  122. 122.
    Zhang, J., Li, L., Zhang, Y., Yang, G., Cao, X., Sun, J.: Video dehazing with spatial and temporal coherence. Vis. Comput. 27(6–8), 749–757 (2011) CrossRefGoogle Scholar
  123. 123.
    Zhang, Y., Tong, R.: Environment-sensitive cloning in images. Vis. Comput. 27(6–8), 739–748 (2011) CrossRefGoogle Scholar
  124. 124.
    Zheng, Y.T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.S., Neven, H.: Tour the world: building a web-scale landmark recognition engine. In: Proceedings of IEEE CVPR, pp. 1085–1092 (2009) Google Scholar
  125. 125.
    Zhong, F., Qin, X., Peng, Q.: Robust image segmentation against complex color distribution. Vis. Comput. 27(6–8), 707–716 (2011) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shi-Min Hu
    • 1
  • Tao Chen
    • 1
  • Kun Xu
    • 1
  • Ming-Ming Cheng
    • 1
  • Ralph R. Martin
    • 2
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.Cardiff UniversityCardiffUK

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