Mining web images for concept learning

Date
2014-08
Advisor
Duygulu, Pınar
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Publisher
Bilkent University
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Language
English
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Abstract

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 concept 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 representativeness of each instance against large number of random images and eliminating the outliers. The final model is used for classification of novel images. These two methods are applied on different benchmark problems and we observed compelling or better results compared to state of art methods.

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Weakly-supervised learning, Concept learning, Rectifying self-organizing map, Association with model evolution, Clustering and outlier detection, Conceptmap, Attributes, Object recognition, Scene classification, Face identification, Feature learning
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Published Version (Please cite this version)