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Semantics Extraction from Images

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Book cover Knowledge-Driven Multimedia Information Extraction and Ontology Evolution

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6050))

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Abstract

An overview of the state-of-the-art on semantics extraction from images is presented. In this survey, we present the relevant approaches in terms of content representation as well as in terms of knowledge representation. Knowledge can be represented in either implicit or explicit fashion while the image is represented in different levels, namely, low-level, intermediate and semantic level. For each combination of knowledge and image representation, a detailed discussion is addressed that leads to fruitful conclusions for the impact of each approach.

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References

  1. Biederman, I., Mezzanotte, R.J., Rabinowitz, J.C.: Scene perception: Detecting and judging objects undergoing relational violations. Cognitive Psychology 14(2), 143–177 (1982)

    Article  Google Scholar 

  2. Hobson, P., Kompatsiaris, Y.: Advances in semantic multimedia analysis for personalised content access. In: ISCAS (2006)

    Google Scholar 

  3. Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part IV. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Sheth, A., Ramakrishnan, C., Christopher, T.: Semantics for the semantic web: The implicit, the formal and the powerful. Int. Journal on Semantic Web and Information Systems 1(1), 1–18 (2005)

    Article  Google Scholar 

  5. Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: CAIVD, pp. 42–51 (1998)

    Google Scholar 

  6. Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10(1), 117–130 (2001)

    Article  MATH  Google Scholar 

  7. Oliva, A., Torralba, A.B.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  8. Huang, F.J., LeCun, Y.: Large-scale learning with svm and convolutional nets for generic object categorization. In: Proc. Computer Vision and Pattern Recognition Conference (CVPR 2006). IEEE Press, Los Alamitos (2006)

    Google Scholar 

  9. Chang, E., Goh, K., Sychay, G., Wu, G.: Cbsa: content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology 13(1), 26–38 (2003)

    Article  Google Scholar 

  10. Pratikakis, I., Gatos, B., Thomopoulos, S.C.: Scene categorisation using low-level visual features. In: VISAPP 2006, vol. 3309, pp. 155–160 (2006)

    Google Scholar 

  11. Serrano, N., Savakis, A.E., Luo, J.: Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition 37(9), 1773–1784 (2004)

    Article  MATH  Google Scholar 

  12. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: International Conference on Computer Vision, vol. 1, pp. 525–531 (2001)

    Google Scholar 

  13. Vogel, J., Schiele, B.: Semantic scene modeling and retrieval for content-based image retrieval. Int. Journal of Computer Vision 72(2), 133–157 (2007)

    Article  Google Scholar 

  14. Oliva, A., Torralba, A.: The role of context in object recognition. Trends in Cognitive Sciences 11(12), 520–527 (2007)

    Article  Google Scholar 

  15. Bosch, A., Muñoz, X., Marti, R.: Which is the best way to organize/classify images by content? Image Vision Comput. 25(6), 778–791 (2007)

    Article  Google Scholar 

  16. Yavlinsky, A., Schofield, E., Rüger, S.M.: Automated image annotation using global features and robust nonparametric density estimation. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 507–517. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Carneiro, G., Vasconcelos, N.: Formulating semantic image annotation as a supervised learning problem. Computer Vision and Pattern Recognition, 163–168 (2005)

    Google Scholar 

  18. Westerveld, T., de Vries, A.P.: Experimental result analysis for a generative probabilistic image retrieval model. In: SIGIR, pp. 135–142 (2003)

    Google Scholar 

  19. Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: MISRM 1999 First International Workshop on Multimedia Intelligent Storage and Retrieval Management, Orlando, FL, USA (1999)

    Google Scholar 

  20. Zhou, X., Wang, M., Zhang, Q., Zhang, J., Shi, B.: Automatic image annotation by an iterative approach: incorporating keyword correlations and region matching. In: CIVR, pp. 25–32 (2007)

    Google Scholar 

  21. Lodhi, H., Shawe-Taylor, J., Cristianini, N., Watkins, C.J.C.H.: Text classification using string kernels. In: NIPS, pp. 563–569 (2000)

    Google Scholar 

  22. Liu, D., Tsuhan, C.: Semantic-shift for unsupervised object detection. In: Workshop on beyond patches in conjunction with CVPR, pp. 16–16 (2006)

    Google Scholar 

  23. Malik, J., Belongie, S., Shi, J., Leung, T.K.: Textons, contours and regions: Cue integration in image segmentation. In: ICCV, pp. 918–925 (1999)

    Google Scholar 

  24. Leung, T.K., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)

    Article  MATH  Google Scholar 

  25. Schmid, C., Mohr, R.: Combining greyvalue invariants with local constraints for object recognition. In: CVPR, pp. 872–877 (1996)

    Google Scholar 

  26. Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: MIR 2005: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262. ACM, New York (2005)

    Google Scholar 

  27. Yu, J., Tian, Q., Amores, J., Sebe, N.: Toward robust distance metric analysis for similarity estimation. In: CVPR (1), pp. 316–322 (2006)

    Google Scholar 

  28. Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: CVPR (2), pp. 1447–1454 (2006)

    Google Scholar 

  29. Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: ICCV (2005)

    Google Scholar 

  30. Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: textonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  31. Winn, J.M., Criminisi, A., Minka, T.P.: Object categorization by learned universal visual dictionary. In: ICCV, pp. 1800–1807 (2005)

    Google Scholar 

  32. Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: CVPR (2), pp. 994–1000 (2005)

    Google Scholar 

  33. van Gemert, J.C., Geusebroek, J.M., Veenman, C.J., Snoek, C.G., Smeulders, A.W.: Robust scene categorization by learning image statistics in context. cvprw 0, 105 (2006)

    Google Scholar 

  34. Jégou, H., Douze, M., Schmid, C.: On the burstiness of visual elements. In: Conference on Computer Vision & Pattern Recognition (June 2009)

    Google Scholar 

  35. Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: ICCV, pp. 1458–1465 (2005)

    Google Scholar 

  36. Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS, pp. 985–992 (2006)

    Google Scholar 

  37. Wu, J., Rehg, J.M.: Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In: IEEE International Conference on Computer Vision (ICCV) (2009)

    Google Scholar 

  38. Kokkinos, I., Maragos, P., Yuille, A.L.: Bottom-up & top-down object detection using primal sketch features and graphical models. In: CVPR (2), pp. 1893–1900 (2006)

    Google Scholar 

  39. Csurka, G., Dance, C., Willamowski, J., Fan, L., Bray, C.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision (2004)

    Google Scholar 

  40. Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, pp. 1150–1157 (1999)

    Google Scholar 

  41. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classifcation of texture and object categories: An in-depth study. Technical Report RR-5737, INRIA, Antipolis (2005) Technical report

    Google Scholar 

  42. Zhang, H., Berg, A., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: CVPR, pp. 2126–2136 (2006)

    Google Scholar 

  43. Yang, C., Dong, M., Hua, J.: Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. In: CVPR (2), pp. 2057–2063 (2006)

    Google Scholar 

  44. Berg, A.C., Malik, J.: Geometric blur for template matching. In: CVPR (1), pp. 607–614 (2001)

    Google Scholar 

  45. Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007)

    Article  Google Scholar 

  46. Niebles, J.C.: A hierarchical model of shape and appearance for human action classification. In: CVPR (2007)

    Google Scholar 

  47. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (June 2003)

    Google Scholar 

  48. Sudderth, E.B., Torralba, A.B., Freeman, W.T., Willsky, A.S.: Learning hierarchical models of scenes, objects, and parts. In: ICCV, pp. 1331–1338 (2005)

    Google Scholar 

  49. Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple object class detection with a generative model. In: CVPR (1), pp. 26–36 (2006)

    Google Scholar 

  50. Amores, J., Sebe, N., Radeva, P.: Class-specific binary correlograms for object recognition. In: BMVC (2007)

    Google Scholar 

  51. Mori, G., Belongie, S., Malik, J.: Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1832–1837 (2005)

    Article  MATH  Google Scholar 

  52. Amores, J., Sebe, N., Radeva, P.: Context-based object-class recognition and retrieval by generalized correlograms. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1818–1833 (2007)

    Article  Google Scholar 

  53. Liu, D., Hua, G., Viola, P., Chen, T.: Integrated feature selection and higher-order spatial feature extraction for object categorization. In: CVPR 2008, pp. 1–8 (2008)

    Google Scholar 

  54. Setia, L., Teynor, A., Halawani, A., Burkhardt, H.: Grayscale medical image annotation using local relational features. Pattern Recognition Letters 29(15), 2039–2045 (2008); Image CLEF 2007 - Automatic annotation of medical images for image retrieval

    Article  Google Scholar 

  55. Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: CVPR 2006, vol. II, pp. 2033–2040 (2006)

    Google Scholar 

  56. Savarese, S., DelPozo, A., Niebles, J., Fei-Fei, L.: Spatial-Temporal correlatons for unsupervised action classification. In: IEEE Workshop on Motion and Video Computing, WMVC 2008, pp. 1–8 (2008)

    Google Scholar 

  57. Lazebnik, S., Schmid, C., Ponce, J.: Semi-local affine parts for object recognition. In: BMVC, pp. 959–968 (2004)

    Google Scholar 

  58. Quack, T., Ferrari, V., Leibe, B., Gool, L.J.V.: Efficient mining of frequent and distinctive feature configurations. In: ICCV, pp. 1–8 (2007)

    Google Scholar 

  59. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2), pp. 2169–2178 (2006)

    Google Scholar 

  60. Ling, H., Soatto, S.: Proximity distribution kernels for geometric context in category recognition. In: ICC 2007, pp. 1–8 (2007)

    Google Scholar 

  61. Bosch, A., Zisserman, A., Muñoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR, pp. 401–408 (2007)

    Google Scholar 

  62. Vedaldi, A., Soatto, S.: Relaxed matching kernels for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  63. Zhang, Y., Chen, T.: Efficient kernels for identifying unbounded-order spatial features, pp. 1762–1769 (2009)

    Google Scholar 

  64. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.J.C.H.: Text classification using string kernels. Journal of Machine Learning Research 2, 419–444 (2002)

    MATH  Google Scholar 

  65. Pantofaru, C., Dorko, G., Schmid, C., Hebert, M.: Combining regions and patches for object class localization. In: The Beyond Patches Workshop in conjunction with the CVPR, pp. 23–30 (June 2006)

    Google Scholar 

  66. Martin, D.R., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–425 (2001)

    Google Scholar 

  67. Yang, L., Meer, P., Foran, D.: Multiple class segmentation using a unified framework over mean-shift patches. In: CVPR 2007, pp. 1–8 (2007)

    Google Scholar 

  68. Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  69. Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models, pp. 119–126 (2003)

    Google Scholar 

  70. Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. In: NIPS (2003)

    Google Scholar 

  71. Feng, S., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: CVPR (2), pp. 1002–1009 (2004)

    Google Scholar 

  72. Tang, J., Hare, J.S., Lewis, P.H.: Image auto-annotation using a statistical model with salient regions. In: ICME, pp. 525–528 (2006)

    Google Scholar 

  73. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  74. Murphy, K., Torralba, A., Freeman, W.: Using the forest to see the trees: a graphical model relating features, objects and scenes. In: NIPS (2003)

    Google Scholar 

  75. Kumar, S., Hebert, M.: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: ICCV, vol. 2, pp. 1150–1157 (2003)

    Google Scholar 

  76. Fei-Fei, L., Fergus, R., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, June 20-25, vol. 2, pp. 524–531 (2005)

    Google Scholar 

  77. Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594–611 (2006)

    Article  Google Scholar 

  78. Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2), pp. 1605–1614 (2006)

    Google Scholar 

  79. Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Gool, L.J.V.: Modeling scenes with local descriptors and latent aspects. In: ICCV, pp. 883–890 (2005)

    Google Scholar 

  80. Barnard, K., Duygulu, P., Forsyth, D.A., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research 3, 1107–1135 (2003)

    MATH  Google Scholar 

  81. Blei, D., Jordan, M.: Modeling annotated data. In: SIGIR 2003. ACM, New York (2003)

    Google Scholar 

  82. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  83. Magalhães, J., Rüger, S.M.: Logistic regression of generic codebooks for semantic image retrieval. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 41–50. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  84. Boutell, M.R., Luo, J., Brown, C.M.: Factor graphs for region-based whole-scene classification. In: CVPR Workshop on Semantic Learning Applications in Multimedia, p. 104 (2006)

    Google Scholar 

  85. Fan, J., Gao, Y., Luo, H., Xu, G.: Statistical modeling and conceptualization of natural images. Pattern Recognition 38(6), 865–885 (2005)

    Article  Google Scholar 

  86. Luo, J., Savakis, A.E., Singhal, A.: A bayesian network-based framework for semantic image understanding. Pattern Recognition 38(6), 919–934 (2005)

    Article  Google Scholar 

  87. Monay, F., Quelhas, P., Odobez, J.M., Gatica-Perez, D.: Integrating co-occurrence and spatial contexts on patchbased scene segmentation. In: CVPR Workshop on Beyond patches. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  88. Wang, G., Zhang, Y., Fei-Fei, L.: Using dependent regions for object categorization in a generative framework. In: CVPR 2006, New York, NY, USA, June 17-22, vol. 2, pp. 1597–1604 (2006)

    Google Scholar 

  89. Shi, J., Malik, J.: Normalized cuts and image segmentation. In: CVPR, pp. 731–737 (1997)

    Google Scholar 

  90. Cao, L., Fei-Fei, L.: Spatially coherent latent topic model for concurrent object segmentation and classification. In: Proceedings of IEEE Intern. Conf. in Computer Vision (ICCV) (2007)

    Google Scholar 

  91. Singhal, A., Luo, J., Zhu, W.: Probabilistic spatial context models for scene content understanding. In: CVPR (1), pp. 235–241 (2003)

    Google Scholar 

  92. Kschischang, F.R., Frey, B.J., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47(2), 498–519 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  93. Neches, R., Fikes, R., Finin, T.W., Gruber, T.R., Patil, R.S., Senator, T.E., Swartout, W.R.: Enabling technology for knowledge sharing. AI Magazine 12(3), 36–56 (1991)

    Google Scholar 

  94. Chandrasekaran, B., Josephson, J.R., Benjamins, V.R.: What are ontologies, and why do we need them? IEEE Intelligent Systems 14(1), 20–26 (1999)

    Article  Google Scholar 

  95. Gruber, T.: Towards principles for the design of ontologies used for knowledge sharing. International Journal for Human-Computer Studies 43, 907–928 (1995)

    Article  Google Scholar 

  96. Guarino, N., Giaretta, P.: Ontologies and knowledge bases: Towards a terminological clarification. In: Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, pp. 25–32 (1995)

    Google Scholar 

  97. Towards text recognition in natural scene images. In: SWAMM 2006, collocated with WWW 2006, Edinburgh, Scotland (2006)

    Google Scholar 

  98. Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: Region-based image retrieval using an object ontology and relevance feedback. EURASIP J. Appl. Signal Process. 2004(1), 886–901 (2004)

    Article  Google Scholar 

  99. Dasiopoulou, S., Mezaris, V., Kompatsiaris, I., Papastathis, V.K., Strintzis, M.G.: Knowledge-assisted semantic video object detection. IEEE Trans. Circuits Syst. Video Techn. 15(10), 1210–1224 (2005)

    Article  Google Scholar 

  100. Maillot, N.: Ontology-based object learning and recognition. PhD thesis, ORION / INRIA Sophia-Antipolis (December 2005)

    Google Scholar 

  101. Hudelot, C., Maillot, N., Thonnat, M.: Symbol grounding for semantic image interpretation: from image data to semantics. In: ICCV, Workshop on Semantic Knowledge in Computer Vision (2005)

    Google Scholar 

  102. Town, C., Sinclair, D.: Language-based querying of image collections on the basis of an extensible ontology. Image Vision Comput. 22(3), 251–267 (2004)

    Article  Google Scholar 

  103. Leibe, B., Schiele, B.: Interleaved object categorization and segmentation. In: BMVC (September 2003)

    Google Scholar 

  104. Harris, C., Stephens, M.: A combined corner and edge detector. In: 4th ALVEY Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  105. Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 113–127. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  106. Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  107. Farmer, M.E., Jain, A.K.: A wrapper-based approach to image segmentation and classification. In: ICPR (2), pp. 106–109 (2004)

    Google Scholar 

  108. Kokkinos, I., Maragos, P.: An expectation maximization approach to the synergy between image segmentation and object categorization. In: ICCV, pp. 617–624 (2005)

    Google Scholar 

  109. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient matching of pictorial structures. In: CVPR, p. 2066 (2000)

    Google Scholar 

  110. Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV 2004 Workshop on Statistical Learning in Computer Vision (May 2004)

    Google Scholar 

  111. Jones, M., Poggio, T.: Multidimensional morphable models: A framework for representing and matching object classes. International Journal of Computer Vision 29, 107–131(25) (1998)

    Article  Google Scholar 

  112. Cootes, T., Taylor, C.: Statistical models of appearance for computer vision. Technical report, University of Manchester, Wolfson Image Analysis Unit, Imaging Science and Biomedical Engineering, Manchester M13 9PT, United Kingdom (September 1999), http://www.wiau.man.ac.uk

  113. Borenstein, E., Malik, J.: Shape guided object segmentation. In: CVPR 2006, pp. 969–976. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  114. Galun, M., Sharon, E., Basri, R., Brandt, A.: Texture segmentation by multiscale aggregation of filter responses and shape elements. In: ICCV, pp. 716–723 (2003)

    Google Scholar 

  115. Ferran, C., Giró, X., Marqués, F., Casas, J.R.: BPT enhancement based on syntactic and semantic criteria. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds.) SAMT 2006. LNCS, vol. 4306, pp. 184–198. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  116. Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing 9(4), 561–576 (2000)

    Article  Google Scholar 

  117. Papadopoulos, G.T., Mezaris, V., Dasiopoulou, S., Kompatsiaris, I.: Semantic image analysis using a learning approach and spatial context. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds.) SAMT 2006. LNCS, vol. 4306, pp. 199–211. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  118. Skiadopoulos, S., Giannoukos, C., Sarkas, N., Vassiliadis, P., Sellis, T., Koubarakis, M.: 2d topological and direction relations in the world of minimum bounding circles. IEEE Transactions on Knowledge and Data Engineering 17(12), 1610–1623 (2005)

    Article  Google Scholar 

  119. Wang, Y., Makedon, F., Ford, J., Shen, L., Goldin, D.Q.: Generating fuzzy semantic metadata describing spatial relations from images using the r-histogram. In: JCDL 2004, Tuscon, AZ, USA, June 7-11, pp. 202–211 (2004)

    Google Scholar 

  120. Sikora, T.: The mpeg-7 visual standard for content description-an overview. IEEE Trans. Circuits Syst. Video Techn. 11(6), 696–702 (2001)

    Article  Google Scholar 

  121. Athanasiadis, T., Mylonas, P., Avrithis, Y.S.: A context-based region labeling approach for semantic image segmentation. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds.) SAMT 2006. LNCS, vol. 4306, pp. 212–225. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  122. Athanasiadis, T., Tzouvaras, V., Petridis, K., Precioso, F., Avrithis, Y., Kompatsiaris, Y.: Using a multimedia ontology infrastructure for semantic annotation of multimedia content. In: Proc. of 5th International Workshop on Knowledge Markup and Semantic Annotation (SemAnnot 2005), Galway, Ireland (November 2005)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Informatics and Telecommunications, NCSR “Demokritos”, 15310, Ag. Paraskevi, Athens, Greece

    Ioannis Pratikakis, Anastasia Bolovinou, Bassilios Gatos & Stavros Perantonis

  2. Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Ioannis Pratikakis

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  3. Bassilios Gatos
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  4. Stavros Perantonis
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Editors and Affiliations

  1. Institute of Informatics and Telecommunications, National Centre for Scientific Research “Demokritos”, P.O. box 60228, 15310, Ag. Paraskevi, Athens, Greece

    Georgios Paliouras  & Constantine D. Spyropoulos  & 

  2. Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-51, 01307, Dresden, Germany

    George Tsatsaronis

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© 2011 Springer-Verlag Berlin Heidelberg

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Pratikakis, I., Bolovinou, A., Gatos, B., Perantonis, S. (2011). Semantics Extraction from Images. In: Paliouras, G., Spyropoulos, C.D., Tsatsaronis, G. (eds) Knowledge-Driven Multimedia Information Extraction and Ontology Evolution. Lecture Notes in Computer Science(), vol 6050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20795-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-20795-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20794-5

  • Online ISBN: 978-3-642-20795-2

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