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):

0

10

20

30

40

50

60

Google

AltaVist

aLy

cosYah

oo

Allthew

ebCNN

Corbis

Findso

unds

3dca

feExc

ite

VastV

ideo

Vivi

simo

Mamma

Que

ries

Answ

ered

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+

05

1015

2025

30

35404550

Google

AltaVist

a

allthe

web.co

m

Lyco

s+

corbi

s.com

Singing

fish.c

om+

Gettyim

age+

Yahoo

CNN

Web

shots

.com+

Que

ries

Answ

ered

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)