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  1. 1. Texture Segmentation for Remote Sensing Image <br />Based on Texture-Topic Model<br />HaoFengZhiguo Jiang<br />Image Processing Center<br />Beijing Universityof Aeronautics & Astronautics <br />Xingmin Han<br />Beijing University of Technology<br />IGARSS 2011<br />
  2. 2.
  3. 3. water<br />sand<br />grass<br />tree 1, high density<br />tree 2, middle density<br />tree 3, low density<br />
  4. 4. Proposed Method<br />-Topic Model: Latent Dirichlet Allocation<br />-LDA is a generative probabilistic model of <br />a corpus. <br /> -LDA automatically clusters words into “topics” <br /> and documents into mixtures of topics.<br />-Bag-of-Words Assumption<br /> - Connecting word and feature descriptor<br />Cluster method<br />---link<br />Pattern from image<br />-Texture is topic, <br /> pixel (feature descriptor) is word.<br />
  5. 5. Previous Works<br /><ul><li> Li Fei-Fei, PietroPerona, CVPR 2005
  6. 6. Supervised LDA
  7. 7. Natural Scene Categorization
  8. 8. Erik B. Sudderth, IJCV 2008
  9. 9. Transformed Dirichlet Process
  10. 10. Model natural scene with spatial constraint
  11. 11. Marie Liénou,…, IEEE Geoscienceand Remote Sensing Letter 2010 </li></ul>DragosBratasanu, Lon Nedelcu, MihaiDatcu, IGARSS 2011<br /><ul><li>Annotation of Satellite Images Using LDA
  12. 12. Xian Sun,…, IEEE Geoscience and Remote Sensing Letter 2010
  13. 13. Model geospatial object using LDA</li></li></ul><li>Latent Dirichlet Allocation<br />-LDA is a generative probabilistic model of a corpus. <br />-Documents are represented as random mixtures over latent topics<br />-where a topic is characterized by a distribution over words.<br /><ul><li>Let’s assume that all the words within a document are exchangeable.</li></li></ul><li>Latent Dirichlet Allocation<br />For each document,<br /><ul><li>Choose  ~ Dirichlet()
  14. 14. For each of the N words :
  15. 15. Choose a topic zn~ Multinomial()
  16. 16. Choose a word from , a multinomial probability conditioned on the topic zn.</li></ul>[blei 2003]<br />
  17. 17. Latent Dirichlet Allocation<br />Topic: Education<br />Frequency<br />……..<br />……..<br />environment<br />student<br />postgraduate<br />undergraduace<br />debt<br />education<br />labor<br />University<br />course<br />Dictionary<br />word<br />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]<br />
  18. 18. Latent Dirichlet Allocation<br />θ<br />Topic Distribution<br />Building 2<br />Building 1<br />z<br />Latent topic<br />Topic 2<br />Topic 3<br />Topic 1<br />w<br />Bag-of-words<br />
  19. 19. Spatial Constraint LDA<br />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.<br />2,600,000,000 results<br />448,000,000 results<br />13,400,000 results<br />57,100 results<br />
  20. 20. Spatial Constraint LDA<br />Neighbors<br />Gaussian <br />Parameters<br />
  21. 21. Spatial Constraint LDA<br />Normal Inverse Wishart<br />Gaussian Distribution<br />Dirichlet Distribution<br />Multinominal Distribution<br />Multinominal Distribution<br />For each image, Choose ~Dirichlet(). <br />2) For each pixel, draw texture-topic zn~ Multinominal() . <br />3) For a topiczn, choose Gaussian parameters<br />4) Choose the visual word <br />5) Given the selected texture-topiczn and word, choose word <br />
  22. 22. Spatial Constraint LDA<br />z<br />w<br />Example:<br />Word<br />Red: Considered Word<br /> (feature Descriptor)<br />r<br />Neighboring words<br />
  23. 23. Classification texture<br />1. Sample Keypoint word<br />2. Sample Neighborhood Word and Variance<br />3. Sample texture from keypoint and neighbor word<br />
  24. 24. Pattern<br />Spatial Constraint LDA<br />z<br />r<br />w<br />Neighboring words<br />Ref Point<br />Feature<br />Descriptor<br />Mean<br />Cov<br />
  25. 25. Experiments<br />Textures Segment<br />Brodatz texture and texture combination<br />4 dimension Haar feature<br />500 words visual dictionary<br />2) Remote Sensing Images<br /> 200 dimension DAISY descriptor<br /> 1000 words visual dictionary<br />
  26. 26. Results<br />
  27. 27. Texture model<br />Texture image<br />Visual word map<br />Texture image<br />Visual word map<br />
  28. 28. Results<br />1<br />2<br />4<br />5<br />3<br />
  29. 29. 1<br />2<br />4<br />5<br />3<br />
  30. 30. Results<br />Tree 1<br />Garss<br />Tree 2<br />Road<br />Tree 3<br />Tree<br />Grassland<br />Rooftop<br />Road/Sand/Land<br />Park<br />
  31. 31. Results<br />
  32. 32. Conclusion<br /><ul><li>Model Texture using LDA
  33. 33. Introduce Neighborhood constraint to LDA
  34. 34. Segment texture combinations and remote sensing </li></ul> images<br />-Noise in sampling results<br />-Bag-of-words<br />-Speed<br />-Feature descriptor<br />-More information….<br />
  35. 35. Thank you<br />fenghao@sa.buaa.edu.cn<br />