Bilkent University
Department of Computer Engineering
MS Thesis Presentation
Mining Web Images for Concept Learning
Eren Gölge
MS Student
Computer Engineering Department
Bilkent University
We attack the problem of learning concepts automatically from noisy Web image search results. The idea is based on discovering common characteristics shared among category images by posing two novel methods that are able to organise the data while eliminating irrelevant instances.We propose a novel clustering and outlier detection method, namely Concept Map (CMAP). Given an image collection returned for a con- cept query, CMAP provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. One another method is Association through Model Evolution (AME). It prunes the data in an iterative manner and it progressively finds better set of images with an evaluational score computed for each iteration. The idea is based on capturing discriminativeness and representative- ness of each instance and eliminating the outliers. Final model is used for classification of novel images. These two method is applied to different benchmark problems and we observed compelling or better results compared to state of art methods.
DATE: 11 August, 2014, Monday @ 10:00
PLACE: EA-409