Kaliciak, Leszek; Song, Dawei; Wiratunga, Nirmalie and Pan, Jeff
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|DOI (Digital Object Identifier) Link:||http://dx.doi.org/10.1145/1871437.1871671|
|Google Scholar:||Look up in Google Scholar|
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
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications|
|Depositing User:||Dawei Song|
|Date Deposited:||21 Jun 2012 09:13|
|Last Modified:||26 Oct 2012 15:42|
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