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Computer Vision, Part 1

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Page 1: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Computer Vision, Part 1

Page 2: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 3: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 4: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 5: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 6: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Topics for Vision Lectures

1. Content-Based Image Retrieval (CBIR)

2. Object recognition and scene “understanding”

Page 7: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Content-Based Image Retrieval

Page 8: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Example: Google “Search by Image”

http://www.google.com/imghp?hl=en&tab=wi
Page 9: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Query Image

Extract Features(Primitives)

Image Database

Features Database

SimilarityMeasure

MatchedResults

RelevanceFeedbackAlgorithm

From http://www.amrita.edu/cde/downloads/ACBIR.ppt

Basic technique

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 vector xi most similar to query vector Q

http://images.google.co.in/imgres?imgurl=http://www.demochoice.org/img/zoo/lion.jpg&imgrefurl=http://www.demochoice.org/dcballot.php?poll=sfzoo&h=389&w=382&sz=31&tbnid=nJfShNo3dQ7iKM:&tbnh=119&tbnw=116&hl=en&start=1&prev=/images?q=lion&svnum=10&hl=en&lr=
Page 10: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

• Distance measure:

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

.

.

.

€

x i⋅Q = x1iQ1 + x2

iQ2 + ...+ xmiQm

Page 11: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 12: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Types of Features Typically Used

• Intensities

• Color

• Texture

• Shape

• Layout

Page 13: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Intensity histogramshttp://www.clear.rice.edu/elec301/Projects02/artSpy/intensity.html

Page 14: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Color Features

Hue, saturation, value

Page 15: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Color Histograms (8 colors)

Page 16: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 17: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Color auto-correlogram

• Pick any pixel p1 of color Ci in the image I.

• At distance k away from p1 pick another pixel p2.

• What is the probability that p2 is also of color Ci?

p1

p2k

Red ?

Image: I

From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt

Page 18: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

• The auto-correlogram of image I for color Ci , distance k:

• Integrates both color information and space information.

]|,|Pr[|)( 1221)(

iii CCkC IpIpkppI

From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt

Page 19: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 20: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Color histogram rank: 411; Auto-correlogram rank: 1

Color histogram rank: 310; Auto-correlogram rank: 5

Color histogram rank: 367; Auto-correlogram rank: 1

From: http://www.cs.cornell.edu/rdz/Papers/ecdl2/spatial.htm

Page 21: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Texture representations

• Gray-level co-occurrence

• Entropy

• Contrast

• Fourier and wavelet transforms

• Gabor filters

Page 22: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Texture Representations

Each image has the same intensity distribution, but different textures

Can use auto-correlogram based on intensity (“gray-level co-occurrence”)

Page 23: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 24: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 25: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Texture from Contrast

• Example: http://www.clear.rice.edu/elec301/Projects02/artSpy/graininess.html

http://www.clear.rice.edu/elec301/Projects02/artSpy/graininess.html
http://www.clear.rice.edu/elec301/Projects02/artSpy/graininess.html
Page 26: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Texture from Wavelets

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

Page 27: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 28: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 29: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 30: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

From: http://www.cs.ucl.ac.uk/staff/k.jacobs/teaching/prmv/Edge_histogramming.jpg

Histogram of edge orientations

Page 31: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Visual abilities largely missing from current CBIR systems

• Object recognition

• Perceptual organization

• Similarity between semantic concepts

Page 32: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Image 1 Image 2

Examples of “semantic” similarity

Page 33: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Image 1 Image 2

Examples of “semantic” similarity

Page 34: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Image 1 Image 2

Examples of “semantic” similarity

Page 35: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

“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.”

Page 36: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 37: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

Image Understanding and Analogy-Making

Page 38: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding

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

Page 39: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 40: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 41: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 42: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 43: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 44: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 45: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
Page 46: Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding
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