spatial semi-supervised image classification
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Spatial Semi-supervised Image Classification. Stuart Ness G07 - Csci 8701 Final Project. 1. Outline. Introduction – Traditional Image Classification Motivation Problem Definition Key Concepts Assumptions Contributions Future Work. 2. Introduction – Traditional Image Classification. - PowerPoint PPT PresentationTRANSCRIPT
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Spatial Semi-supervised Image Classification
Stuart Ness
G07 - Csci 8701 Final Project
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Outline Introduction – Traditional Image
Classification
Motivation
Problem Definition
Key Concepts
Assumptions
Contributions
Future Work 2
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Introduction – Traditional Image Classification
The Classification Problem
How would you begin to classify this data given the following information?− The classes are:
Building = 1 Forest = 2 ???? = 3 Sand = 4 Water = 5 Grass = 6
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Introduction: Supervised
− The resulting classifier is: Building = 1 = Red and Orange Forest = 2 = Green Sand = 4 = Aqua Water = 5 = Blue Grass = 6 = Yellow
Requires extensive domain knowledge 4
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Introduction: Unsupervised
Provide the data
Provide a method forclustering
Create Groups− Group ‘A’ = Red -Group ‘B’ = Yellow− Group ‘D’ = Blue -Group ‘C’ = Orange− Group ‘E’ = Aqua -Group ‘F’ = Green− Group ‘G’ = Purple
Domain Expert must classify each group
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Motivation Problems with Traditional Methods
− Supervised requires extensive domain knowledge
− Supervised may create bias due to the selection of labeled points
− Unsupervised may not have the correct model specified
− Computationally expensive due to no initial estimates
Project goal is to identify the work of semi-supervised learning that may be applied to a spatial context
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Problem Definition: Semi-Supervised Learning
Given− Set of Labeled Data (Supervised)− Set of Unlabeled Data (Unsupervised)
Find− Fast and accurate method for
classifying data
Objectives− Speed− Little need for Domain Expert Data
Constraints− Spatial Data
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Key Concepts Semi-supervised learning has been
studied in the textual domain− Spatial Significance
Semi-Supervised Process (typical)− Select Data Points (Labeled and
Unlabeled)− Create an initial Cluster with labeled
data points and/or probability function
− Cluster Data Samples to create classifier
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Key Concepts: Extensions
Pair-wise relation Co-Training
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Same Land Types
Different Land Types
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Key Concepts: Extensions
Markov Random Fields− General Classification
−Image from http://www.etro.vub.ac.be/Research/IRIS/Research/MVISION/MRF%20models.htm
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Key Concepts: Extensions
Neighborhood EM−Include information from
surrounding areas
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Key Concepts: Extensions
Hybrid EM
− Attempt at improving efficiency
− Reduce number of iterations from neighborhood EM
− Deals with spatial Data unlike normal EM
− Use traditional EM unless expectation decreases then use neighborhood EM
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Assumptions Unlabeled Samples are Inexpensive
− Not Guaranteed
− Unlabeled samples may not belong to labeled Class (Purple Class – Snow) may require extra processing to examine
− Randomly chosen unlabeled samples eliminate bias, but are there benefits to using a set of randomly chosen clusters of points
Local Maximum from Hill Climbing is sufficient
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Contributions Provide a brief summary of semi-
supervised methods that pertain to the spatial domain
Identify problems of existing semi-supervised method− Unlabeled Samples− Local Maximum
Identify extensions from textual domain which could be applied to a spatial context− Co-training & Neighborhood EM− Markov Random Fields− Hybrid EM
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Future Work Deal with the problems of randomly
sampled unlabeled data− Random Sample− Random Cluster Sample− Choosing samples from known
classes
Improve Algorithm Efficiency
Implement non-hill climbing approach for finding global maximum
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Conclusion
Semi-supervised learning is fairly well developed.
Minimal work has been done to implement “spatial” features of method although, background is ready
Selecting Unlabeled Samples, Choosing the correct model, and local maximum are problematic
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