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 for clustering  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 9 Same Land Types Different Land Types

10. Key Concepts: Extensions  Markov Random Fields −General Classification −Image from http://www.etro.vub.ac.be/Resear ch/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