Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback
IEEE Transactions on Multimedia
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Current approaches to automatic, class specific, image retrieval from the World Wide Web (WWW) by linguistic query often make use of an image's internal characteristics and file meta-data to augment and improve result accuracy. We propose that, in extension, improvement can be achieved in relevance, noise-reduction and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query homographs and image context prior to any additional filtering. Within the paper we investigate different schemes for keyword set construction; ontology exclusive and authority extended, along with three differing ranking mechanisms.