Image Processing and Computer Vision
description
Transcript of 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
Similar color distribution
Similar texture pattern
Similar shape/pattern
Similar real content
Degree of difficulty
Histogram matching
Texture analysis
Image Segmentation,Pattern recognition
Life-time goal :-)
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)
Reference: “A Review of CBIR”
Recommended reading:
A Review of Content-Based Image Retrieval SystemsColin C. Venters and Dr. Matthew Cooper, University of ManchesterAvailable 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.
Search Engines Used by 2001 Multimedia Class
• Search Engines used for 2001 multimedia retrieval homework (15 others answered a single query each):
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AltaVist
aLy
cosYah
oo
Allthew
ebCNN
Corbis
Findso
unds
3dca
feExc
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VastV
ideo
Vivi
simo
Mamma
Que
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Answ
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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+
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AltaVist
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allthe
web.co
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Lyco
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corbi
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Singing
fish.c
om+
Gettyim
age+
Yahoo
CNN
Web
shots
.com+
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Answ
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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 Intelligence 18(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
For Further Exploration of Image Segmentation
• BlobWorld work at UC Berkeley• Papers, description, sample system available
at http://elib.cs.berkeley.edu/photos/blobworld/
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
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
Decomposition in Resolution/Frequency
fine
fine
coarse intermediate
intermediate
Wavelet Decomposition
Vertical subbands (LH)
Wavelet Decomposition
Horizontalsubbands (HL)