SIFT and color feature fusion using localized maximum-margin learning for scene classification

By Jianzhao Qin, Nelson Hong Ching Yung. 2011

7 citations recorded by the source corpus.

In this paper, we proposed a method that uses localized maximum-margin learning to fuse SIFT and color features during the bags of visual words modeling process for eventual scene classification. It offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Barnab'as P'oczos
2. Jeff Schneider
3. Danica J. Sutherland
4. Liang Xiong
5. Krikamol Muandet
6. Kirthevasan Kandasamy
7. Junier B. Oliva
8. Jorge Guevara

Citations in the corpus

• Support Distribution Machines (2012, background): “Qin and Yung (2010a) proposed another way to encode the spatial dependencies of patches into the visual words and achieved 90 .”