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Computer Vision, Part 1

Computer Vision, Part 1. Topics for Vision Lectures. Content-Based Image Retrieval (CBIR) Object recognition and scene “understanding” . Content-Based Image Retrieval. Example: Google “Search by Image” . Extract Features (Primitives). Similarity Measure. Matched Results. Query Image.

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Computer Vision, Part 1

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  1. Computer Vision, Part 1

  2. Topics for Vision Lectures • Content-Based Image Retrieval (CBIR) • Object recognition and scene “understanding”

  3. Content-Based Image Retrieval

  4. Example: Google “Search by Image”

  5. Extract Features (Primitives) Similarity Measure Matched Results Query Image Image Database Relevance Feedback Algorithm Features Database Basic technique From http://www.amrita.edu/cde/downloads/ACBIR.ppt Each image in database is represented by a feature vector:x1,x2, ...xN, where xi = (xi1, xi2, …, xim) Query is represented in terms of same features: Q =(Q1, Q2, …, Qm) Goal: Find stored image with vectorximost similar to query vector Q

  6. Distance measure: • Possible distance measures d(Q, xi): • Inner (dot) product • Histogram distance (for histogram features) • Graph matching (for shape features) . . .

  7. Some issues in designing a CBIR system • Query format, ease of querying • Speed • Crawling, preprocessing • Interactivity, user relevance feedback • Visual features — which to use? How to combine? • Curse of dimensionality • Indexing • Evaluation of performance

  8. Types of Features Typically Used • Intensities • Color • Texture • Shape • Layout

  9. http://www.clear.rice.edu/elec301/Projects02/artSpy/intensity.htmlhttp://www.clear.rice.edu/elec301/Projects02/artSpy/intensity.html Intensity histograms

  10. Color Features Hue, saturation, value

  11. Color Histograms (8 colors) http://www.owlnet.rice.edu/~elec301/Projects02/artSpy/patmac/mcolhist.gif

  12. http://www.owlnet.rice.edu/~elec301/Projects02/artSpy/patmac/mcolhist.gifhttp://www.owlnet.rice.edu/~elec301/Projects02/artSpy/patmac/mcolhist.gif

  13. Color auto-correlogram • Pick any pixelp1of color Ci in the image I. • At distance k away fromp1pick another pixelp2. • What is the probability thatp2is also of color Ci? Red ? k p2 p1 Image: I From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt

  14. The auto-correlogram of image I for color Ci, distancek: • Integrates both color information and space information. From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt

  15. Two images with their autocorrelograms. Note that the change in spatial layout would be ignored by color histograms, but causes a significant difference in the autocorrelograms. From http://www.cs.cornell.edu/rdz/Papers/ecdl2/spatial.htm

  16. From: http://www.cs.cornell.edu/rdz/Papers/ecdl2/spatial.htm Color histogram rank: 411; Auto-correlogramrank: 1 Color histogram rank: 310; Auto-correlogramrank: 5 Color histogram rank: 367; Auto-correlogramrank: 1

  17. Texture representations • Gray-level co-occurrence • Entropy • Contrast • Fourier and wavelet transforms • Gabor filters

  18. Texture Representations Each image has the same intensity distribution, but different textures Can use auto-correlogram based on intensity (“gray-level co-occurrence”)

  19. Texture from entropy Images filtered by entropy: Each output pixel contains entropy value of 9x9 neighborhood around original pixel From: http://www.siim2011.org/abstracts/advanced_visualization_tools_ss_pao.html

  20. Texture from fractal dimension From: http://www.cs.washington.edu/homes/rahul/data/iccv07.pdf

  21. Texture from Contrast • Example: http://www.clear.rice.edu/elec301/Projects02/artSpy/graininess.html

  22. Texture from Wavelets http://www.clear.rice.edu/elec301/Projects02/artSpy/dwt.html

  23. http://www.clear.rice.edu/elec301/Projects02/artSpy/dwt.html

  24. Shape representations Some of these need segmentation (another whole story!) • Area, eccentricity, major axis orientation • Skeletons, shock graphs • Fourier transformation of boundary • Histograms of edge orientations

  25. From: http://www.lems.brown.edu/vision/researchAreas/ShockMatching/shock-ed-match-results1.gif

  26. Histogram of edge orientations From: http://www.cs.ucl.ac.uk/staff/k.jacobs/teaching/prmv/Edge_histogramming.jpg

  27. Visual abilities largely missing from current CBIR systems • Object recognition • Perceptual organization • Similarity between semantic concepts

  28. Examples of “semantic” similarity Image 1 Image 2

  29. Examples of “semantic” similarity Image 1 Image 2

  30. Examples of “semantic” similarity Image 1 Image 2

  31. “In general, current systems have not yet had significant impact on society due to an inability to bridge the semantic gap between computers and humans.”

  32. Image Understanding and Analogy-Making

  33. Bongard problems as an idealized domain for exploring the “semantic gap”

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