ConceptMap: mining noisy web data for concept learning

Date
2014-09
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Source Title
13 th European Conference on Computer Vision Computer Vision – ECCV 2014
Print ISSN
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Publisher
Springer
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Pages
439 - 455
Language
English
Type
Conference Paper
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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 subsets of images by posing a method that is 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. The proposed method outperforms the state-of-the-art studies on the task of learning from noisy web data for low-level attributes, as well as high level object categories. It is also competitive with the supervised methods in learning scene concepts. Moreover, results on naming faces support the generalisation capability of the CMAP framework to different domains. CMAP is capable to work at large scale with no supervision through exploiting the available sources. © 2014 Springer International Publishing.

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Keywords
Attributes, Clustering and outlier detection, ConceptMap, Object detection, Scene classification, Semi- supervised model learning, Weakly-labelled data, Artificial intelligence, Computer science, Computers, Model learning, Outlier Detection, Statistics
Citation
Published Version (Please cite this version)