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Novel local features with hybrid sampling technique for image retrieval

Kaliciak, Leszek; Song, Dawei; Wiratunga, Nirmalie and Pan, Jeff (2010). Novel local features with hybrid sampling technique for image retrieval. In: 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), 26–30 October 2010, Toronto, Ontario, Canada.

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DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1145/1871437.1871671
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Abstract

In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, when it comes to the retrieval of generic real-life images, randomly generated patches are often more discriminant than the ones produced by corner/blob detectors. In order to tackle these problems, we propose a novel method incorporating local features with a hybrid sampling (a combination of detector-based and random sampling). We take three large data collections for the evaluation: MIRFlickr, ImageCLEF, and a collection from British National Geological Survey. The overall performance of the proposed approach is better than the performance of global features and comparable with the current state-of-the-art methods in content-based image retrieval. One of the advantages of our method when compared with others is its easy implementation and low computational cost. Another is that hybrid sampling can improve the performance of other methods based on the "bag of visual words" approach.

Item Type: Conference Item
Copyright Holders: 2010 ACM
Extra Information: CIKM '10
Proceedings of the 19th ACM International Conference on Information and Knowledge Management
New York, NY, ACM ©2010
ISBN: 978-1-4503-0099-5
doi>10.1145/1871437.1871671
pp. 1557-1560
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Item ID: 33904
Depositing User: Dawei Song
Date Deposited: 21 Jun 2012 09:13
Last Modified: 26 Oct 2012 15:42
URI: http://oro.open.ac.uk/id/eprint/33904
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