project slides

Report
Bag-of-Words based
Image Classification
Joost van de Weijer
What is in the image ?
Is there a suit-case ?
Is there a person ?
Is there car ?
image classification: answers the question what is in the image.
Inspiration
The VOC Pascal challenge: a competition on image classification.
Participants have to classify 20 classes in over 10.000 images.
Inspiration
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2010/results/index.html
The Event Data Set
• 7 event classes: basketball, polo, rowing, castells, marathon, sailing, skiing.
• each class has 50 images, devided in 30 training and 20 test images.
Project I
title: Bag-of-Words based Image Classification.
goal: build an image classification system which can successfully
classify sport images.
competition: do so better than the other groups.
Why is this difficult ?
Variations in
viewpoint and zoom.
Variations in pose.
Why is this difficult ?
Inter-class variation.
lighting changes.
Why is this difficult ?
Back-ground
variation.
similar backgroundsdifferent classes.
Maybe the background could help ?
from images to frequency histogram
•Compute visual words:
• detect local regions from a set of images.
• describe every local region by a descriptor
• texture
• color
• cluster all descriptors into visual words
Given a new image:
• detect local regions from a set of image.
• assign every region to its nearest visual word.
• compute visual word-image histogram
assign to visual
word N
Bag of Visual Words representation
Bag-of-Words
representation
Feature Detection
normalize
patches
No spatial relations.
Bag of Visual Words representation
pi(w|Miro)
pi(w|Dali)
The Framework
4. BOW
1. Feature
detection
Image
Image
Representation
5. SVM/ distance
measures
2. Extraction
shape
texture
color
Shape Voc
image classification
image retrieval
shape words
Existing Implementation:
1. Feature
detection
1.random
4. BOW
4. nearest
neighbor
Image
2. RGB
Image
Representation
5. linear SVM
2. Extraction
shape
texture
color
image classification
image retrieval
50 % classification
score
3. random
Shape Voc
5. SVM/ distance
measures
shape words
Existing Implementation:
properties of BOW implementation:
• you can improve any of the subroutines and analyze the changes
based on the classification results.
• several team members can work on feature detection while others work
on feature description.
• the final classification results allow us to compare the results between
the groups.
Project I: Bag Bag-of-Words based
Image Classification
goal : build an image classification system which can successfully
classify sport images.
teaching objectives
you will learn:
• to represent images robust to changes of cameras, object orientation, and illuminant color.
• what photometric invariance theory is and how to apply it to a real-world problem.
• understand and use the SIFT descriptor.
• how to discretize image features (colors, shapes, and textures).
• what the strong and weak points of BOW representations for images are.
• how to evaluate retrieval and classification results.
Practical information:
Group Size:
The project has to be made in groups of 3 students. Each group
should decide on the following roles:
• responsible competition.
• responsible presentation.
• responsible report
If it is hard to work as a group you can partition the tasks:
• feature detection
• feature description
• vocabulary construction
• learning/evaluation
All group-members should
understand all steps in the
final program !
Practical Information:
All practical information can be found in the student guide
(http://cat.cvc.uab.es/~joost/master.html )
Practical information:
Important Dates:
22 jan - 19 Feb.
22 jan.
29 Jan.
5 Feb.
11 Feb.
12 Feb.
15 Feb.
19 Feb.
22 Feb.
: The project will last 1 month.
: Start project.
: Extra assignment will be handed out. Submission of first results in AP.
: Discussion meeting + submission second results in AP.
: Publication of final test set.
: Discussion meeting with groups separately.
: Final submission of classification results in AP for all classes.
: Presentation of the project.
: Final submission date for report.
Supervision:
There will be project meetings on Tuesdays afternoon to discus progress.
For any questions during the three weeks of the project email ([email protected]) or
come to office O/119 in the CVC.
Use “PROJECT I” as subject of your emails, which makes it easier to manage.
Practical information:
Notes
The final note will be based on:
• participation (15%)
• presentation (25%)
• report (50%)
• competition (10%)
Bugs:
For sure there will be several bugs in the code. If you find one, mail
me, and I will notify the other groups. Thanks !
Practical information:
Competition:
Dates:
29 Jan.
5 Feb.
: Submission of first results in AP (before 15:00).
: Submission second results in AP (before 15:00)
19/22 Feb.
: Your report/final presentation is based on the labeled test set !
labeled train set
labeled test set
Practical information:
Competition:
Dates:
11 Feb.
15 Feb.
: Publication of final test set.
: Final submission results in AP for all calsses.
labeled train set
no labels for test set !
Practical information:
Final Report
The final report has to be submitted on 22th of February. The report should
contain the following chapters.
• Introduction ( max 1 page )
• Feature Detection (max 2 pages).
• Feature Description (max 3 pages).
• Visual Vocabulary and BOW representation (max 2 pages)
• Classification (max 2 pages)
• Object Detection (optional: max 2 pages)
• Results (max 2 pages).
• Conclusions (max 1 page)
What to do next ?
• make groups of and assign :
•responsible competition
(send an email to me today or tomorrow )
• install the programs and play with the code.
( http://cat.cvc.uab.es/~joost/master.html )
• This week you should already start working on a feature detector.
What to do next ?
Good Luck !

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