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IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
IGARSS11_haofeng_final.pptx
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IGARSS11_haofeng_final.pptx

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  • 1. Texture Segmentation for Remote Sensing Image
    Based on Texture-Topic Model
    HaoFengZhiguo Jiang
    Image Processing Center
    Beijing Universityof Aeronautics & Astronautics
    Xingmin Han
    Beijing University of Technology
    IGARSS 2011
  • 2.
  • 3. water
    sand
    grass
    tree 1, high density
    tree 2, middle density
    tree 3, low density
  • 4. Proposed Method
    -Topic Model: Latent Dirichlet Allocation
    -LDA is a generative probabilistic model of
    a corpus.
    -LDA automatically clusters words into “topics”
    and documents into mixtures of topics.
    -Bag-of-Words Assumption
    - Connecting word and feature descriptor
    Cluster method
    ---link
    Pattern from image
    -Texture is topic,
    pixel (feature descriptor) is word.
  • 5. Previous Works
    • Li Fei-Fei, PietroPerona, CVPR 2005
    • 6. Supervised LDA
    • 7. Natural Scene Categorization
    • 8. Erik B. Sudderth, IJCV 2008
    • 9. Transformed Dirichlet Process
    • 10. Model natural scene with spatial constraint
    • 11. Marie Liénou,…, IEEE Geoscienceand Remote Sensing Letter 2010
    DragosBratasanu, Lon Nedelcu, MihaiDatcu, IGARSS 2011
    • Annotation of Satellite Images Using LDA
    • 12. Xian Sun,…, IEEE Geoscience and Remote Sensing Letter 2010
    • 13. Model geospatial object using LDA
  • Latent Dirichlet Allocation
    -LDA is a generative probabilistic model of a corpus.
    -Documents are represented as random mixtures over latent topics
    -where a topic is characterized by a distribution over words.
    • Let’s assume that all the words within a document are exchangeable.
  • Latent Dirichlet Allocation
    For each document,
    • Choose  ~ Dirichlet()
    • 14. For each of the N words :
    • 15. Choose a topic zn~ Multinomial()
    • 16. Choose a word from , a multinomial probability conditioned on the topic zn.
    [blei 2003]
  • 17. Latent Dirichlet Allocation
    Topic: Education
    Frequency
    ……..
    ……..
    environment
    student
    postgraduate
    undergraduace
    debt
    education
    labor
    University
    course
    Dictionary
    word
    This will mean that the Open University, which provides degreecourses by distance learning, will have among the lowest fees in England. Vice chancellor Martin Bean promised "high-quality, flexible and great value-for-money education for all". The majority of universities will charge £9,000 for some or all courses. More than two-thirds of the Open University'sstudents are studying part-time - and the university will be expecting to benefit from the introduction of loans for part-time students. For a typical part-time Open University student, studying at the level of half of full-time, the fees will be £2,500 per year. MrBean said that the extension of the loan system represented the "beginning of a new era for part-time students". Younger studentsAt present the university has 264,000 students taking more than 600 undergraduate and postgraduatecourses and professional qualifications - ……. [BBC News]
  • 18. Latent Dirichlet Allocation
    θ
    Topic Distribution
    Building 2
    Building 1
    z
    Latent topic
    Topic 2
    Topic 3
    Topic 1
    w
    Bag-of-words
  • 19. Spatial Constraint LDA
    The William Randolph Hearst Foundation will give $1.25 million to Lincoln Center, Metropolitan Opera Co., New York Philharmonic and Juilliard School. “Our board felt that we had a real opportunity to make a mark on the future of the performing arts with these grants an act every bit as important as our traditional areas of support in health, medical research, education and the social services,” Hearst Foundation President Randolph A. Hearst said Monday in announcing the grants. Lincoln Center’s share will be $200,000 for its new building, which will house young artists and provide new public facilities. The Metropolitan Opera Co. and New York Philharmonic will receive $400,000 each. The Juilliard School, where music and the performing arts are taught, will get $250,000. The Hearst Foundation, a leading supporter of the Lincoln Center Consolidated Corporate Fund, will make its usual annual $100,000 donation, too.
    2,600,000,000 results
    448,000,000 results
    13,400,000 results
    57,100 results
  • 20. Spatial Constraint LDA
    Neighbors
    Gaussian
    Parameters
  • 21. Spatial Constraint LDA
    Normal Inverse Wishart
    Gaussian Distribution
    Dirichlet Distribution
    Multinominal Distribution
    Multinominal Distribution
    For each image, Choose ~Dirichlet().
    2) For each pixel, draw texture-topic zn~ Multinominal() .
    3) For a topiczn, choose Gaussian parameters
    4) Choose the visual word
    5) Given the selected texture-topiczn and word, choose word
  • 22. Spatial Constraint LDA
    z
    w
    Example:
    Word
    Red: Considered Word
    (feature Descriptor)
    r
    Neighboring words
  • 23. Classification texture
    1. Sample Keypoint word
    2. Sample Neighborhood Word and Variance
    3. Sample texture from keypoint and neighbor word
  • 24. Pattern
    Spatial Constraint LDA
    z
    r
    w
    Neighboring words
    Ref Point
    Feature
    Descriptor
    Mean
    Cov
  • 25. Experiments
    Textures Segment
    Brodatz texture and texture combination
    4 dimension Haar feature
    500 words visual dictionary
    2) Remote Sensing Images
    200 dimension DAISY descriptor
    1000 words visual dictionary
  • 26. Results
  • 27. Texture model
    Texture image
    Visual word map
    Texture image
    Visual word map
  • 28. Results
    1
    2
    4
    5
    3
  • 29. 1
    2
    4
    5
    3
  • 30. Results
    Tree 1
    Garss
    Tree 2
    Road
    Tree 3
    Tree
    Grassland
    Rooftop
    Road/Sand/Land
    Park
  • 31. Results
  • 32. Conclusion
    • Model Texture using LDA
    • 33. Introduce Neighborhood constraint to LDA
    • 34. Segment texture combinations and remote sensing
    images
    -Noise in sampling results
    -Bag-of-words
    -Speed
    -Feature descriptor
    -More information….
  • 35. Thank you
    fenghao@sa.buaa.edu.cn

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