1 / 17

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.

cicily
Download Presentation

Image Processing and Computer Vision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related