Statistical Pattern Recognition
A Brief Overview of the course
HamidR. Rabiee
JafarMuhammadi, NimaPourdamghani
Spring 2012
Agenda
??What is a Pattern?
??What is Pattern Recognition (PR)???Applications of PR
??Components of a PR system??Features
??Types of Learning
??The Design Cycle
??Pattern Recognition Approaches??Brief Mathematical Overview??Course Road Map
2Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
What is a pattern?
??Pattern
??Opposite to chaos; it is an entity, object, process or event, vaguely defined, that can be given a name or “label”.
??For example, a pattern could be
??A fingerprint image
??A handwritten cursive word
??A human face
??A speech signal
??Texture
??Etc.
3Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
What is Pattern Recognition?
??Pattern recognition (PR)
??The study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns.
??The assignment of a physical object or event to one of several pre-specified categories
??Related terms
??A pattern class(or category) is a set of patterns sharing common attributes and usually originating from the same source.
??During recognition(or classification) given objects are assigned to prescribed classes(get labeled).
??A classifieris a machine which performs classification.
4Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
An Example
??Four pattern categories (classes)
??Sea, Beach, Jungle, Sky
??Common attributes (features)
??Color
??Contrast
??Texture
??Goal
??Observing some labeled pixels, We wish to assign a
label to each new (unlabeled) pixel.
5Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Applications of PR
??Document Classification (Web news classification)
??Input Pattern: Text or html document
??Output Classes: Semantic Categories (e.g. business, sports, …)??Financial Time Series Prediction
??Input Pattern: relation between consecutive data of time series??Output Values: possible values of output (regression problem)??Sequence Analysis (Bioinformatics)
??Input Pattern: DNA / protein sequences
??Output: Known types of genes
??Spam Detection
??Input Pattern: Text / image of emails
??Output Classes: Spam / not spam
7Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Components of a PR system??Example: Separate different types of fishes
??Sensor:
??Camera
??Preprocessing:
??Segmentation
??Features:
??Ask experts the major differences between types
??See different fishes and find the differences
??Length, Width, Number of fins, …
??Learning:
??Ask experts the type of sample fishes
??Find typical length of each type
??Classification:
??Compare the length (width, etc) of a new fish to the learned lengths
9Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Features
??Feature is any distinctive aspect, quality or characteristic
??Features may be symbolic (i.e., color) or numeric (i.e, height)
??Definitions
??The combination of d feature is represented as a d-dimensional column vector called a
?????x1??x??2???xd??
Feature vector
10Feature spaceScatter plotSharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Features
??“Fish Separation”Example:
??The length is a poor feature alone!
??
Select the lightness as a possible feature11Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Good/Bad Features & Classification
??The quality of a feature vector is related to its ability to discriminate examples
from different classes
??
??Examples from the same class should have similar feature valuesExamples from the different classes have different feature values
??The distinction between good and poor features (a), and feature properties (b)
Good featuresBad features
(a)
Linear
SeparablilityNon-linear SeparabilityMulti modalHighly correlated
(b)
13Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
Feature Dimension
??The curse of dimensionality
??The probability of misclassification of a decision rule does notdecrease beyond a certain
dimension for the feature space as the number of features increases.
??Peaking Phenomena
??
Adding features may actually degrade the performance of a classifier
14Sharif University of Technology, Computer Engineering Department, Pattern Recognition Course
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