Skip to main content

Introduction

  • Chapter
  • First Online:
  • 1024 Accesses

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

As re-occurring compositions of visual data, visual patterns exist in complex spatial structures and diverse feature views of image and video data. Discovering visual patterns is of great interest to visual data analysis and recognition. Many methods have been proposed to address the problem of visual pattern discovery during the dozen years. In this chapter, we start with an overview of the visual pattern discovery problem and then discuss the major progress of spatial and feature co-occurrence pattern discovery.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Akata, Z., Thurau, C., Bauckhage, C., et al.: Non-negative matrix factorization in multimodality data for segmentation and label prediction. In: Proceedings of Computer Vision Winter Workshop (2011)

    Google Scholar 

  2. Bagon, S., Brostovski, O., Galun, M., Irani, M.: Detecting and sketching the common. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 33–40 (2010)

    Google Scholar 

  3. Blaschko, M., Lampert, C.: Correlational spectral clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Cai, X., Nie, F., Huang, H., Kamangar, F.: Heterogeneous image feature integration via multi-modal spectral clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1977–1984 (2011)

    Google Scholar 

  6. Cai, Z., Wang, L., Peng, X., Qiao, Y.: Multi-view super vector for action recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  7. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Proceedings of European Conference on Computer Vision, pp. 778–792 (2010)

    Google Scholar 

  8. Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: Proceedings of International Conference on Machine Learning, pp. 129–136 (2009)

    Google Scholar 

  9. Cho, M., Shin, Y.M., Lee, K.M.: Unsupervised detection and segmentation of identical objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1617–1624 (2010)

    Google Scholar 

  10. Cong, Y., Yuan, J., Luo, J.: Towards scalable summarization of consumer videos via sparse dictionary selection. IEEE Trans. Multimed. 14(1), 66–75 (2012)

    Article  Google Scholar 

  11. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)

    Google Scholar 

  12. Diba, A., Pazandeh, A.M., Pirsiavash, H., Gool, L.V.: Deepcamp: deep convolutional action and attribute mid-level patterns. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3557–3565 (2016)

    Google Scholar 

  13. Elhamifar, E., Sapiro, G., Vidal, R.: See all by looking at a few: sparse modeling for finding representative objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1600–1607 (2012)

    Google Scholar 

  14. Eynard, D., Kovnatsky, A., Bronstein, M.M., Glashoff, K., Bronstein, A.M.: Multimodal manifold analysis by simultaneous diagonalization of laplacians. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2505–2517 (2015)

    Article  Google Scholar 

  15. Faktor, A., Irani, M.: “Clustering by composition”-unsupervised discovery of image categories. In: Proceedings of European Conference on Computer Vision, pp. 474–487 (2012)

    Google Scholar 

  16. Fang, Z., Cao, Z., Xiao, Y., Zhu, L., Yuan, J.: Adobe boxes: locating object proposals using object adobes. IEEE Trans. Image Process. 25(9), 4116–4128 (2016)

    MathSciNet  Google Scholar 

  17. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: CVPR Workshop on Generative-Model Based Vision, pp. 178–178 (2004)

    Google Scholar 

  18. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  19. Fernando, B., Fromont, E., Tuytelaars, T.: Mining mid-level features for image classification. Int. J. Comput. Vis. 108(3), 186–203 (2014)

    Article  MathSciNet  Google Scholar 

  20. Fidler, S., Leonardis, A.: Towards scalable representations of object categories: learning a hierarchy of parts. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  21. Gao, J., Hu, Y., Liu, J., Yang, R.: Unsupervised learning of high-order structural semantics from images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2122–2129 (2009)

    Google Scholar 

  22. Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)

    MathSciNet  MATH  Google Scholar 

  23. Grauman, K., Leibe, B.: Visual Object Recognition (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, San Rafael, CA (2011)

    Google Scholar 

  24. Guo, X., Liu, D., Jou, B., Zhu, M., Cai, A., Chang, S.F.: Robust object co-detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  25. Guo, Y.: Convex subspace representation learning from multi-view data. In: Proceedings of AAAI Conference on Artificial Intelligence (2013)

    Google Scholar 

  26. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)

    Article  MathSciNet  Google Scholar 

  27. Heinly, J., Dunn, E., Frahm, J.M.: Comparative evaluation of binary features. In: Proceedings of European Conference on Computer Vision, pp. 759–773 (2012)

    Google Scholar 

  28. Hong, P., Huang, T.: Spatial pattern discovery by learning a probabilistic parametric model from multiple attributed relational graphs. Discret. Appl. Math. 139(1), 113–135 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  29. Hsu, W., Dai, J., Lee, M.: Mining viewpoint patterns in image databases. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 553–558 (2003)

    Google Scholar 

  30. Huang, H.C., Chuang, Y.Y., Chen, C.S.: Affinity aggregation for spectral clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 773–780 (2012)

    Google Scholar 

  31. Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3304–3311 (2010)

    Google Scholar 

  32. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093 (2014)

  33. Jiang, Y., Liu, J., Li, Z., Li, P., Lu, H.: Co-regularized plsa for multi-view clustering. In: Proceedings of Asian Conference on Computer Vision, pp. 202–213 (2012)

    Google Scholar 

  34. Kim, S., Jin, X., Han, J.: Disiclass: discriminative frequent pattern-based image classification. In: KDD Workshop on Multimedia Data Mining, pp. 7:1–7:10 (2010)

    Google Scholar 

  35. Kobayashi, T.: Low-rank bilinear classification: efficient convex optimization and extensions. Int. J. Comput. Vis. 110(3), 308–327 (2014)

    Article  MATH  Google Scholar 

  36. Kong, Y., Fu, Y.: Bilinear heterogeneous information machine for rgb-d action recognition. In: CVPR, pp. 1054–1062 (2015)

    Google Scholar 

  37. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  38. Kumar, A., III, H.D.: A co-training approach for multi-view spectral clustering. In: Proceedings of International Conference on Machine Learning, pp. 393–400 (2011)

    Google Scholar 

  39. Kumar, A., Rai, P., III, H.D.: Co-regularized multi-view spectral clustering. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1413–1421 (2011)

    Google Scholar 

  40. Lange, T., Buhmann, J.M.: Fusion of similarity data in clustering. In: Proceedings of Advances in Neural Information Processing Systems (2005)

    Google Scholar 

  41. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1482–1489 (2005)

    Google Scholar 

  42. Li, C., Parikh, D., Chen, T.: Automatic discovery of groups of objects for scene understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  43. Li, Y., Liu, L., Shen, C., van den Hengel, A.: Mining mid-level visual patterns with deep cnn activations. Int. J. Comput. Vis. 121(3), 344–364 (2017)

    Article  MathSciNet  Google Scholar 

  44. Li, Y., Wang, S., Tian, Q., Ding, X.: A survey of recent advances in visual feature detection. Neurocomputing 149, 736–751 (2015)

    Article  Google Scholar 

  45. Liu, D., Chen, T.: A topic-motion model for unsupervised video object discovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA (2007)

    Google Scholar 

  46. Liu, H., Yan, S.: Common visual pattern discovery via spatially coherent correspondences. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1609–1616 (2010)

    Google Scholar 

  47. Liu, J., Liu, Y.: Grasp recurring patterns from a single view. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  48. Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of SIAM International Conference on Data Mining (2013)

    Google Scholar 

  49. Long, B., Philip, S.Y., Zhang, Z.M.: A general model for multiple view unsupervised learning. In: Proceedings of SIAM International Conference on Data Mining, pp. 822–833 (2008)

    Google Scholar 

  50. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  51. Meng, J., Wang, H., Yuan, J., Tan, Y.P.: From keyframes to key objects: video summarization by representative object proposal selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1039–1048 (2016)

    Google Scholar 

  52. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1–2), 43–72 (2005)

    Article  Google Scholar 

  53. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of International Conference on Machine Learning, pp. 689–696 (2011)

    Google Scholar 

  54. Oramas, J.M., Tuytelaars, T.: Modeling visual compatibility through hierarchical mid-level elements. arXiv:1604.00036 (2016)

  55. Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  56. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Proceedings of European Conference on Computer Vision, pp. 143–156 (2010)

    Google Scholar 

  57. Philbin, J., Sivic, J., Zisserman, A.: Geometric latent dirichlet allocation on a matching graph for large-scale image datasets. Int. J. Comput. Vis. 95(2), 138–153 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  58. Quack, T., Ferrari, V., Leibe, B., Van Gool, L.: Efficient mining of frequent and distinctive feature configurations. In: Proceedings of IEEE International Conference on Computer Vision (2007)

    Google Scholar 

  59. Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  60. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

  61. Russell, B., Freeman, W., Efros, A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1605–1614 (2006)

    Google Scholar 

  62. de Sa, V.R., Gallagher, P.W., Lewis, J.M., Malave, V.L.: Multi-view kernel construction. Mach. Learn. 79(1–2), 47–71 (2010)

    MathSciNet  Google Scholar 

  63. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  64. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 (2013)

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

    Article  Google Scholar 

  66. Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 370–377 (2005)

    Google Scholar 

  67. Sivic, J., Russell, B., Zisserman, A., Freeman, W., Efros, A.: Unsupervised discovery of visual object class hierarchies. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  68. Sivic, J., Zisserman, A.: Video data mining using configurations of viewpoint invariant regions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 488–495 (2004)

    Google Scholar 

  69. Srivastava, N., Salakhutdinov, R.: Multimodal learning with deep boltzmann machines. J. Mach. Learn. Res. 15(1), 2949–2980 (2014)

    MathSciNet  MATH  Google Scholar 

  70. Sun, M., hamme, H.V.: Image pattern discovery by using the spatial closeness of visual code words. In: Proceddings of IEEE International Conference on Image Processing, Brussels, Belgium, pp. 205–208 (2011)

    Google Scholar 

  71. Tang, J., Lewis, P.H.: Non-negative matrix factorisation for object class discovery and image auto-annotation. In: Proceedings of the International Conference on Content-based Image and Video Retrieval, Niagara Falls, Canada, pp. 105–112 (2008)

    Google Scholar 

  72. Thompson, D.W.: On Growth and Form. Cambridge University Press, Cambridge, UK (1961)

    Google Scholar 

  73. Todorovic, S., Ahuja, N.: Unsupervised category modeling, recognition, and segmentation in images. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2158–2174 (2008)

    Article  Google Scholar 

  74. Tuytelaars, T., Lampert, C., Blaschko, M., Buntine, W.: Unsupervised object discovery: a comparison. Int. J. Comput. Vis. 88(2), 284–302 (2010)

    Article  Google Scholar 

  75. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Foundations and Trends® in Computer Graphics and Vision 3(3), 177–280 (2008)

    Google Scholar 

  76. Wang, B., Jiang, J., Wang, W., Zhou, Z.H., Tu, Z.: Unsupervised metric fusion by cross diffusion. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2997–3004 (2012)

    Google Scholar 

  77. Wang, G., Zhang, Y., Fei-Fei, L.: Using dependent regions for object categorization in a generative framework. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1597–1604 (2006)

    Google Scholar 

  78. Wang, H., Kawahara, Y., Weng, C., Yuan, J.: Representative selection with structured sparsity. Pattern Recognit. 63, 268–278 (2017)

    Article  Google Scholar 

  79. Wang, H., Nie, F., Huang, H.: Multi-view clustering and feature learning via structured sparsity. In: Proceedings of International Conference on Machine Learning (2013)

    Google Scholar 

  80. Wang, H., Nie, F., Huang, H., Ding, C.: Heterogeneous visual features fusion via sparse multimodal machine. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  81. Wang, H., Yuan, J., Tan, Y.: Combining feature context and spatial context for image pattern discovery. In: Proceedings of IEEE International Conference on Data Mining, pp. 764–773 (2011)

    Google Scholar 

  82. Wang, H., Yuan, J., Wu, Y.: Context-aware discovery of visual co-occurrence patterns. IEEE Trans. Image Process. 23(4), 1805–1819 (2014)

    Article  MathSciNet  Google Scholar 

  83. Wang, H., Zhao, G., Yuan, J.: Visual pattern discovery in image and video data: a brief survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 4(1), 24–37 (2014)

    Article  Google Scholar 

  84. Wang, W., Arora, R., Livescu, K., Bilmes, J.A.: On deep multi-view representation learning: objectives and optimization. arXiv: 1602.01024 (2016)

  85. Wang, X., Grimson, E.: Spatial latent dirichlet allocation. In: Proceedings of Advances in Neural Information Processing Systems (2008)

    Google Scholar 

  86. Wang, X., Qian, B., Ye, J., Davidson, I.: Multi-objective multi-view spectral clustering via pareto optimization. In: Proceedings of SIAM International Conference on Data Mining (2013)

    Google Scholar 

  87. Weng, C., Wang, H., Yuan, J., Jiang, X.: Discovering class-specific spatial layouts for scene recognition. IEEE Sig. Process. Lett. (2016)

    Google Scholar 

  88. Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv:1304.5634 (2013)

  89. Xu, C., Tao, D., Xu, C.: Large-margin multi-view information bottleneck. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1559–1572 (2014)

    Article  Google Scholar 

  90. Xu, C., Tao, D., Xu, C.: Multi-view intact space learning. IEEE Trans. Pattern Anal. Mach. Intell. 37, 2531–2544 (2015)

    Article  Google Scholar 

  91. Yang, J., Wang, Z., Lin, Z., Shu, X., Huang, T.: Bilevel sparse coding for coupled feature spaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2360–2367 (2012)

    Google Scholar 

  92. Yu, S., Tranchevent, L.C., Liu, X., Glanzel, W., Suykens, J.A., De Moor, B., Moreau, Y.: Optimized data fusion for kernel k-means clustering. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1031–1039 (2012)

    Article  Google Scholar 

  93. Yuan, J.: Discovering visual patterns in image and video data: concepts, algorithms, experiments. VDM Verlag Dr. Müller, Saarbrcken, Germany (2011)

    Google Scholar 

  94. Yuan, J., Wu, Y.: Spatial random partition for common visual pattern discovery. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  95. Yuan, J., Wu, Y.: Context-aware clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  96. Yuan, J., Wu, Y.: Mining visual collocation patterns via self-supervised subspace learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 1–13 (2012)

    MathSciNet  Google Scholar 

  97. Yuan, J., Zhao, G., Fu, Y., Li, Z., Katsaggelos, A., Wu, Y.: Discovering thematic objects in image collections and videos. IEEE Trans. Image Process. 21, 2207–2219 (2012)

    Article  MathSciNet  Google Scholar 

  98. Zhang, S., Yang, M., Cour, T., Yu, K., Metaxas, D.N.: Query specific fusion for image retrieval. In: Proceedings of European Conference on Computer Vision, pp. 660–673 (2012)

    Google Scholar 

  99. Zhang, S., Yang, M., Wang, X., Lin, Y., Tian, Q.: Semantic-aware co-indexing for image retrieval. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1673–1680 (2013)

    Google Scholar 

  100. Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 809–816 (2011)

    Google Scholar 

  101. Zhao, G., Yuan, J.: Discovering thematic patterns in videos via cohesive sub-graph mining. In: Proceedings of IEEE International Conference on Data Mining, pp. 1260–1265 (2011)

    Google Scholar 

  102. Zhao, G., Yuan, J., Hua, G.: Topical video object discovery from key frames by modeling word co-occurrence prior. IEEE Trans. Image Process. (2015)

    Google Scholar 

  103. Zhao, G., Yuan, J., Xu, J., Wu, Y.: Discovery of the thematic object in commercial videos. IEEE Multimed. Mag. 18(3), 56–65 (2011)

    Article  Google Scholar 

  104. Zhao, J., Xie, X., Xu, X., Sun, S.: Multi-view learning overview: recent progress and new challenges. Inf. Fusion 38, 43–54 (2017)

    Article  Google Scholar 

  105. Zheng, L., Wang, S., Liu, Z., Tian, Q.: Packing and padding: coupled multi-index for accurate image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1947–1954 (2014)

    Google Scholar 

  106. Zhu, S., Guo, C., Wang, Y., Xu, Z.: What are textons? Int. J. Comput. Vis. 62(1), 121–143 (2005)

    Article  Google Scholar 

  107. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Proceedings of European Conference on Computer Vision, pp. 391–405 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongxing Wang .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 The Author(s)

About this chapter

Cite this chapter

Wang, H., Weng, C., Yuan, J. (2017). Introduction. In: Visual Pattern Discovery and Recognition. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4840-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4840-1_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4839-5

  • Online ISBN: 978-981-10-4840-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics