A novel nonparametric approach for saliency detection using multiple features

Authors:
Xin He;Huiyun Jing;Qi Han;Xiamu Niu
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

This paper presents a novel saliency detection approach using multiple features. There are three types of features to be extracted from a local region around each pixel, including intensity, color and orientation. Principal Component Analysis(PCA) is employed to reduce the dimension of the generated feature vector and kernel density estimation is used to measure saliency. We compare our method with five classical methods on a publicly available data set. Experiments on human eye fixation data demonstrate that our method performs better than other methods.