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Discriminative Structure for Visual Signal Understanding

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High-Dimensional and Low-Quality Visual Information Processing

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

This chapter presents a computational model to address one prominent psychological behavior of human beings to recognize images. The basic pursuit of our method can be concluded as that differences among multiple images help visual recognition. Generally speaking, we propose a statistical framework to distinguish what kind of image features capture sufficient category information and what kind of image features are common ones shared in multiple classes. Mathematically, the whole formulation is subject to a generative probabilistic model. Meanwhile, a discriminative functionality is incorporated into the model to interpret the differences among all kinds of images. The whole Bayesian formulation is solved in an Expectation-Maximization paradigm. After finding those discriminative patterns among different images, we design an image categorization algorithm to interpret how these differences help visual recognition within the bag-of-feature framework. The proposed method is verified on a variety of image categorization tasks including outdoor scene images, indoor scene images as well as the airborne SAR images from different perspectives.

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Notes

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    This chapter is reproduced from [1], an open access article with the authors’ own copyright.

References

  1. Deng Y, Zhao Y, Liu Y, Dai Q (2013) Differences help recognition: a probabilistic interpretation. PLoS One 8(6):e63385

    Article  Google Scholar 

  2. Deng Y, Li Y, Qian Y, Ji X, Dai Q (2014) Visual words assignment via information-theoretic manifold embedding. To appear in IEEE Transactions on Cybernetics

    Google Scholar 

  3. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1. Springer Series in Statistics, Berlin

    Google Scholar 

  4. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, Massachusetts

    Google Scholar 

  5. Deng Y, Dai Q, Zhang Z (2011) Graph laplace for occluded face completion and recognition. IEEE Trans Image Process 20(8):2329–2338

    Google Scholar 

  6. Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, ser. ICML ’01. Morgan Kaufmann Publishers Inc, San Francisco, USA, pp 282–289. [Online]. Available at http://dl.acm.org/citation.cfm?id=645530.655813

  7. Pan L, Chu W, Saragih J, De la Torre F, Xie M (2011) Fast and robust circular object detection with probabilistic pairwise voting. IEEE Signal Process Lett 18(11):639–642

    Article  Google Scholar 

  8. Deng Y, Dai Q, Wang R, Zhang Z (2012) Commute time guided transformation for feature extraction. Comput Vision Image Underst 116(4):473–483

    Article  Google Scholar 

  9. Liu R, Lin Z, Su Z, Tang K (2010) Feature extraction by learning Lorentzian metric tensor and its extensions. Pattern Recogn 43(10):3298–3306

    Article  MATH  Google Scholar 

  10. Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society conference on computer vision and pattern recognition, vol 2. IEEE, pp 524–531

    Google Scholar 

  11. Quattoni A, Torralba A (2009) Recognizing indoor scenes. In: IEEE conference on computer vision and pattern recognition, IEEE, pp 413–420

    Google Scholar 

  12. Libioulle C, Louis E, Hansoul S, Sandor C, Farnir F, Franchimont D, Vermeire S, Dewit O, De Vos M, Dixon A et al (2007) Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13. 1 and modulates expression of PTGER4. PLoS Genet 3(4):e58

    Article  Google Scholar 

  13. Brynedal B, Duvefelt K, Jonasdottir G, Roos I, Ã…kesson E, Palmgren J, Hillert J (2007) HLA-A confers an HLA-DRB1 independent influence on the risk of multiple sclerosis. PLoS One 2(7):e664

    Article  Google Scholar 

  14. LaFramboise T, Weir B, Zhao X, Beroukhim R, Li C, Harrington D, Sellers W, Meyerson M (2005) Allele-specific amplification in cancer revealed by SNP array analysis. PLoS Comput Biol 1(6):e65

    Article  Google Scholar 

  15. Shoemaker B, Panchenko A (2007) Deciphering protein–protein interactions. part ii. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol 3(4):e43

    Article  Google Scholar 

  16. Borman S (2009) The expectation maximization algorithm-a short tutorial. Technical report

    Google Scholar 

  17. Bishop C (2006) Pattern recognition and machine learning, vol 4, no 4. Springer, New York

    Google Scholar 

  18. van Gemert JC, Veenman CJ, Smeulders AW, Geusebroek J-M (2010) Visual word ambiguity. IEEE Trans Pattern Anal Mach Intell 32(7):1271–1283

    Google Scholar 

  19. Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 1794–1801

    Google Scholar 

  20. Svetlana L, Cordelia S, Jean P (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society conference on computer vision and pattern recognition, vol 2. IEEE, pp 2169–2178

    Google Scholar 

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Authors and Affiliations

  1. Automation Department, Tsinghua Nationality Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China

    Yue Deng

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  1. Yue Deng
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Correspondence to Yue Deng .

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© 2015 Springer-Verlag Berlin Heidelberg

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Deng, Y. (2015). Discriminative Structure for Visual Signal Understanding. In: High-Dimensional and Low-Quality Visual Information Processing. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44526-6_5

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  • DOI: https://doi.org/10.1007/978-3-662-44526-6_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44525-9

  • Online ISBN: 978-3-662-44526-6

  • eBook Packages: EngineeringEngineering (R0)

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