Regular Articles

Saliency modeling via outlier detection

[+] Author Affiliations
Chuanbo Chen

Huazhong University of Science and Technology, College of Software Engineering, Luoyu Road No. 1037, Wuhan 430074, China

He Tang

Huazhong University of Science and Technology, College of Software Engineering, Luoyu Road No. 1037, Wuhan 430074, China

Zehua Lyu

Huazhong University of Science and Technology, College of Software Engineering, Luoyu Road No. 1037, Wuhan 430074, China

Hu Liang

Huazhong University of Science and Technology, College of Computer Science and Technology, Luoyu Road No. 1037, Wuhan 430074, China

Jun Shang

Huazhong University of Science and Technology, College of Computer Science and Technology, Luoyu Road No. 1037, Wuhan 430074, China

Mudar Serem

Huazhong University of Science and Technology, College of Software Engineering, Luoyu Road No. 1037, Wuhan 430074, China

J. Electron. Imaging. 23(5), 053023 (Oct 23, 2014). doi:10.1117/1.JEI.23.5.053023
History: Received May 23, 2014; Revised August 27, 2014; Accepted September 4, 2014
Text Size: A A A

Abstract.  Based on the fact that human attention is more likely to be attracted by different objects or statistical outliers of a scene, a bottom-up saliency detection model is proposed. Our model regards the saliency patterns of an image as the outliers in a dataset. For an input image, first, each image element is described as a feature vector. The whole image is considered as a dataset and an image element is classified as a saliency pattern if its corresponding feature vector is an outlier among the dataset. Then, a binary label map can be built to indicate the salient and the nonsalient elements in the image. According to the Boolean map theory, we compute multiple binary maps as a set of Boolean maps which indicate the outliers in multilevels. Finally, we linearly fused them into the final saliency map. This saliency model is used to predict the human eye fixation, and has been tested on the most widely used three benchmark datasets and compared with eight state-of-the-art saliency models. In our experiments, we adopt the shuffled the area under curve metric to evaluate the accuracy of our model. The experimental results show that our model outperforms the state-of-the-art models on all three datasets.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Chuanbo Chen ; He Tang ; Zehua Lyu ; Hu Liang ; Jun Shang, et al.
"Saliency modeling via outlier detection", J. Electron. Imaging. 23(5), 053023 (Oct 23, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.5.053023


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Spatial modeling and classification of corneal shape. IEEE Trans Inf Technol Biomed 2007;11(2):203-12.
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.