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An improved Bag-of-Words framework for remote sensing image retrieval in large-scale image databases.
- Source :
-
International Journal of Digital Earth . Apr2015, Vol. 8 Issue 4, p273-292. 20p. - Publication Year :
- 2015
-
Abstract
- Due to advances in satellite and sensor technology, the number and size of Remote Sensing (RS) images continue to grow at a rapid pace. The continuous stream of sensor data from satellites poses major challenges for the retrieval of relevant information from those satellite datastreams. The Bag-of-Words (BoW) framework is a leading image search approach and has been successfully applied in a broad range of computer vision problems and hence has received much attention from the RS community. However, the recognition performance of a typical BoW framework becomes very poor when the framework is applied to application scenarios where the appearance and texture of images are very similar. In this paper, we propose a simple method to improve recognition performance of a typical BoW framework by representing images with local features extracted from base images. In addition, we propose a similarity measure for RS images by counting the number of same words assigned to images. We compare the performance of these methods with a typical BoW framework. Our experiments show that the proposed method has better recognition performance than that of the BoW and requires less storage space for saving local invariant features. [ABSTRACT FROM PUBLISHER]
- Subjects :
- *REMOTE sensing
*COMPUTER vision
*VIRTUAL reality
*IMAGE retrieval
*IMAGE databases
Subjects
Details
- Language :
- English
- ISSN :
- 17538947
- Volume :
- 8
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- International Journal of Digital Earth
- Publication Type :
- Academic Journal
- Accession number :
- 101713055
- Full Text :
- https://doi.org/10.1080/17538947.2014.882420