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Image Processing and Computer Vision

Image Processing and Computer Vision. Outline. Research in Image Processing and Computer Vision Finding Images Content-based Image Retrieval. Find Images With Similar Colors. Find Images with Similar Shape. Goal: Find Images with Similar Content. Spectrum of Content-Based Image Retrieval.

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Image Processing and Computer Vision

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  1. Image Processing and Computer Vision

  2. Outline • Research in Image Processing and Computer Vision • Finding Images • Content-based Image Retrieval

  3. Find Images With Similar Colors

  4. Find Images with Similar Shape

  5. Goal: Find Images with Similar Content

  6. Spectrum of Content-Based Image Retrieval Similar color distribution Histogram matching Similar texture pattern Texture analysis Image Segmentation, Pattern recognition Similar shape/pattern Degree of difficulty Life-time goal :-) Similarrealcontent

  7. Status of Image Search • Typical Search Features • Color • Texture • Shape • Spatial attributes (local color regions, less common than global color, texture, shape metrics) • Commercial Activity • eVision (notes that “visual search engine market segment is projected to reach $1.4 billion by 2005 according to the McKenna Group” http://www.evisionglobal.com/about/index.html • Virage (www.virage.com) • IBM (QBIC part of database toolset)

  8. Reference: “A Review of CBIR” Recommended reading: A Review of Content-Based Image Retrieval Systems Colin C. Venters and Dr. Matthew Cooper, University of Manchester Available at http://www.jisc.ac.uk/jtap/htm/jtap-054.html This review lists features from a number of image retrieval systems, along with heuristic evaluations on the interfaces for a subset of these systems.

  9. Search Engines Used by 2001 Multimedia Class • Search Engines used for 2001 multimedia retrieval homework (15 others answered a single query each):

  10. Search Engines Used in This 2002 Class Also answering 1 query each were: Excite+, Rexfeature, Webseek+, search.netscape.com+, animalplanet.com+, ask.com, naver.com+

  11. For Further Reading on Texture Search • Texture Search: “Texture features for browsing and retrieval of image data”, B.S. Manjunath and W.Y. Ma, IEEE Trans. on Pattern Analysis and Machine Intelligence18(8), Aug. 1996, pp. 837-842. • Texture search via http://www.engin.umd.umich.edu/ceep/tech_day/2000/reports/ECEreport2/ECEreport2.htm (texture features include coarseness, average gray scale value, and number of horizontal and vertical extrema of a specific image region) • For QBIC, texture search works on global coarseness, contrast and directionality features

  12. For Further Exploration of Image Segmentation • BlobWorld work at UC Berkeley • Papers, description, sample system available at http://elib.cs.berkeley.edu/photos/blobworld/

  13. Further Reading on Wavelet Compression and JPEG 2000 • http://www.gvsu.edu/math/wavelets/student_work/EF/how-works.html • http://www-ise.stanford.edu/class/psych221/00/shuoyen/ • Henry Schneiderman Ph.D. Thesis “A Statistical Approach to 3D Object Detection Applied to Faces and Cars”, http://www.ri.cmu.edu/pub_files/pub2/schneiderman_henry_2000_2/schneiderman_henry_2000_2.pdf • http://www.jpeg.org/JPEG2000.html

  14. Summary: Image Processing & Computer Vision • Not as mature as speech recognition • Technology not as reliable • Fewer companies, fewer products • Success on limited problems, e.g., documents • More applicable to fault tolerant problems • Technology will grow • Emergence of digital camera • Improved methods

  15. coarse intermediate fine intermediate fine Decomposition in Resolution/Frequency

  16. Wavelet Decomposition Vertical subbands (LH)

  17. Wavelet Decomposition Horizontalsubbands (HL)

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