Abstract
Interest points have been used as local features with success in many computer vision applications such as image/video retrieval and object recognition. However, a major issue when using this approach is a large number of interest points detected from each image and created a dense feature space. This influences the processing speed in any runtime application. Selecting the most important features to reduce the size of the feature space will solve this problem. Thereby this raises a question of what makes a feature more important than the others? In this paper, we present a new technique to choose a subset of features. Our approach differs from others in a fact that selected feature is based on the context of the given image. Our experimental results show a significant reduction rate of features while preserving the retrieval performance.
Original language | English |
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Title of host publication | Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications |
Number of pages | 5 |
Publisher | IEEE Computer Society Press |
Publication date | 2009 |
Pages | 529-534 |
ISBN (Print) | 9780769538723 |
Publication status | Published - 2009 |
Event | IEEE proceeding of the International Conference on Intelligent Systems Design and Applications - Pisa, Italy Duration: 30 Nov 2009 → 2 Dec 2009 Conference number: 9 |
Conference
Conference | IEEE proceeding of the International Conference on Intelligent Systems Design and Applications |
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Number | 9 |
Country/Territory | Italy |
City | Pisa |
Period | 30/11/2009 → 02/12/2009 |
Keywords
- Image retrieval
- interest points detection