1. IMAGE PROCESSING RECOGNITION AND CLASSIFICATIONBy
DR. FERDA ERNAWAN
Faculty of Computer Systems & Software Engineering

2. Contents This lecture will cover:
Introduction to Patterns and Recognition
Classification vs Clustering
Features
Classification Types
Template Matching
Learning Outcomes:
To expose pattern recognition application

3. Pattern
Pattern recognition covers representation of patterns, clustering and classification (Murty and Devi, 2015)

4. Recognition Identification of a pattern
Clustering (Unsupervised: learning categories)
Classification (Supervised: known categories)
Unsupervised learning
Patterns in data
Clusters
Supervised learning
Providing labels
New data
Training-> test
Classify + regress

5. Unsupervised Learning
The system has to learn the classifier from unlabeled data
The system should estimate the probability density function of the data
Unsupervised learning approach covers:
Clustering (e.g. mixture models, hierarchical clustering, K-means)
Techniques for dimensionality reduction (e.g. singular value decomposition, factorization, principle component analysis)

6. K-mean Clustering K-mean clustering needs a set of n-dimensional
k denotes the number of desired cluster
K-mean algorithm partitions the vectors into clusters, such that is over all clusters, minimizes the sum, of the within-cluster sums of point-to-cluster-centroid distances.

7. Supervised Pattern Recognition
The system has some important properties such as it has known description or it has a set of samples for each properties
A classifier determines a set of designated classes or to the reject class from the inputs vector. For example: Decision tree, nearest class mean.

8. Classification vs. Clustering
Classification is a set of patterns that has been classified
A set of patterns is called a set of training
The learning strategy
Learning can also be unsupervised
In this case, there is no training set
Instead, the classes itself based on a patterns.
Category “A”
Category “B”
Classification
Clustering

9. Classification
Classification is a group based on similarity of items (objects/patterns/image regions/pixels).

10. Pattern Recognition
Given an input pattern, make a decision about the “category” or “class” of the pattern

11. Approaches Statistical Pattern :
The patterns are generated by a probabilistic system
Pattern classes represented by statistical measures.
Structural (or Syntactic) Pattern :
The structural pattern process is based on the structural interrelationships of features.
Pattern classes represented by means of formal structures (e.g. strings, automata, grammars).

12. Features
Features are the individual measurable properties being observed.
A set of features utilized for pattern recognition is called feature vector.
The feature number used is called the dimensionality of the feature vector.
n-dimensional feature vectors represent n-dimensional feature space.

13. Features
height
weight
Class 1
Class 2
Class 1
Class 2

14. Features
Feature extraction aims to create discriminative features for classification
Criteria as good features is given as:
Properties of objects have similar feature values from the same class.
Properties of objects have different feature values from different classes.
Good features
Bad features

15. Features Use fewer features if possible
Use features to differentiate classes
Character recognition example
Sample of good features: aspect ratio, presence of loops
Sample of bad features: number of connected components, number of black pixels.

16. Identify the class of a given pattern
Classifier
Identify the class of a given pattern

17. Classifier Feature Vectors Distance Nearest neighbor classification:
Find closest to the input pattern.
Classify class to the same class as closest.

18. Classifier K-Nearest Classifier
Use k nearest neighbors instead of 1 to classify pattern.
Class 1
Class 2

19. Classifier
A classifier partitions feature into class-labeled regions as shown in below:
and
The classification process determines feature vector x belongs to a region.
Borders between regions are called decision boundaries

20. A Pattern Recognition System
Two Modes:

21. Classification Type

22. Simple Classification: Threshold

23. Pixel and Object Classification
Pixel-wise classification
Characteristics aspect.
Uses color, spectral information, texture, intensity, etc.
Object-wise classification
Physical aspect.
Uses mean intensity, mean color, size, shape, etc. to describe patterns.

24. Object-wise and Pixel-wise Classification

25. Pixel-wise Classification
Pixels haven’t classified in the image.
Extract features (e.g. temporal changes, gray-level representation of texture, color).
Conduct training for classifier.
New input data is classified by classifier.

26. Pixel-wise Classification
Given a color image with 256x256 pixels.
3 features (blue, green and red components).
4 classes (stalk, stamen, background and leaf).

27. Object-wise Classification
Image is segmented into regions and label them. These are the patterns to be classified.
Extract (calculate) features for each pattern.
Train a classifier with class-labelled to determine discriminant in the feature space.
For new input data, determine their classes using discriminant function.

28. Object-wise Classification

29. Classification - Example
Sorting Fish based on species.
Determine Salmon or Sea Bass.

30. Classification - Example
Features extraction of fish include:
Lightness of fish
Position of the mouth
Length of fish
Number and shape of fins
Width

31. Classification - Example

32. Select Features
Select the lightness as a possible feature for classification.
The length of fish is a poor feature for classification.

33. Classification - Example
x = [x1, x2]
FISH
Lightness
Width

34. Classification - Example

35. Classification - Example
Other features can be added in classification process, while the feature should not reduce the performance (e.g. noisy features).
A good decision boundary should provides an optimal performance.

36. Classification - Example
Generalization?

37. Template Matching

38. Template Matching
Finding matches are conducted by normalized correlation (NC).

39. Pseudocode for Template Matching
[location,d] = template(image, template)
[nx ny] = size(image)
[tx ty] = size(template)
for i = tx:(nx-tx)
for j = ty:(ny-ty)
d(i,j) = distance(template, window(i,j)
end
location = index_of_minimum(d(i,j)) % locate minimum distance

40. Example of a “Nose Template”
t2= i2straight(2,1).image(35:45,27:37);

41. References
M.N. Murty and V.S. Devi. Introduction to pattern recognition and machine learning. World Scientific Publishing. ISBN:
R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. Second Edition, John Wiley & Sons ISBN: