Translator Disclaimer
1 April 2010 Fusing local and global information for scene classification
Author Affiliations +
Abstract
We propose to fuse an image's local and global information for scene classification. First, the image's local information is represented by context information exploited using spatial pyramid matching. Images are segmented to patches by a regular grid, and scale invariant feature transform (SIFT) features are extracted. All the patch features are clustered and quantified to get visual words. The visual word pair and visual word triplet are neighboring and different visual words. By an analogy between image pixel space and patch space, we also get visual word groups, which are the continuous occurrence of the same visual words. The spatial envelope is employed for extracting an image's global information. The spatial envelope is a holistic description of the scene, where local information is not taken into account. Finally, a stacked-support vector machine (SVM) fusion method is used to get the scene classification results. Experimented with three benchmark data sets, the results demonstrated that our methods could get better results than most popular scene classification methods presented in recent years.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Yue Jiang, Jin Chen, and Runsheng Wang "Fusing local and global information for scene classification," Optical Engineering 49(4), 047001 (1 April 2010). https://doi.org/10.1117/1.3366666
Published: 1 April 2010
JOURNAL ARTICLE
10 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Image retrieval algorithm using fractal dimensions
Proceedings of SPIE (July 30 2002)
An effective method of eliminating ghosting in image mosaic
Proceedings of SPIE (November 29 2012)

Back to Top