Spatial Semi-supervised Image Classification

1. Spatial Semi-supervised Image Classification Stuart Ness G07 - Csci 8701 Final Project 1

2. Outline • Introduction – Traditional Image Classification • Motivation • Problem Definition • Key Concepts • Assumptions • Contributions • Future Work 2

3. 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 3

4. 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

5. 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 5

6. 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 6

7. 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 7

8. 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 8

9. Key Concepts: Extensions • Pair-wise relation Co-Training Same Land Types Different Land Types 9

10. Key Concepts: Extensions • Markov Random Fields • General Classification • Image from http://www.etro.vub.ac.be/Research/IRIS/Research/MVISION/MRF%20models.htm 10

11. Key Concepts: Extensions • Neighborhood EM • Include information from surrounding areas 11

12. 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 12

13. 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 13

14. 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

15. 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 15

16. 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 16