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Unsupervised Categorization (Filtering) of Google Images Based on Visual Consistency

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the output of image search engines, e.g. Google Images, bypassing the need to manually collect large quantities of training data. This model can then be used to refine the quality of the image search, or to search through other sources of images. This integrated scheme has been implemented and optimized to be used in The Semantic Robot Vision Challenge as a new test-bed for research in the areas of image understanding and knowledge retrieval in large unstructured image databases.

Original languageEnglish (US)
Title of host publicationProceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008
PublisherAAAI press
Pages1796-1797
Number of pages2
ISBN (Electronic)9781577353683
StatePublished - 2008
Externally publishedYes
Event23rd AAAI Conference on Artificial Intelligence, AAAI 2008 - Chicago, United States
Duration: Jul 13 2008 → Jul 17 2008

Publication series

NameProceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008

Conference

Conference23rd AAAI Conference on Artificial Intelligence, AAAI 2008
Country/TerritoryUnited States
CityChicago
Period7/13/08 → 7/17/08

ASJC Scopus subject areas

  • Artificial Intelligence

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