Saliency modeling via outlier detection

Chuanbo Chen; He Tang; Zehua Lyu; Hu Liang; Jun Shang; Mudar Serem

doi:10.1117/1.JEI.23.5.053023

23 October 2014 Saliency modeling via outlier detection

Chuanbo Chen, He Tang, Zehua Lyu, Hu Liang, Jun Shang, Mudar Serem

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 5, 053023 (October 2014). https://doi.org/10.1117/1.JEI.23.5.053023

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.

Citation Download Citation

Chuanbo Chen, He Tang, Zehua Lyu, Hu Liang, Jun Shang, and Mudar Serem "Saliency modeling via outlier detection," Journal of Electronic Imaging 23(5), 053023 (23 October 2014). https://doi.org/10.1117/1.JEI.23.5.053023

Published: 23 October 2014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available