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Journal of Electronic Imaging

Saliency modeling via outlier detection
Author(s): Chuanbo Chen; He Tang; Zehua Lyu; Hu Liang; Jun Shang; Mudar Serem
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Paper 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.

Paper Details

Date Published: 23 October 2014
PDF: 8 pages
J. Electron. Imaging. 23(5) 053023 doi: 10.1117/1.JEI.23.5.053023
Published in: Journal of Electronic Imaging Volume 23, Issue 5
Show Author Affiliations
Chuanbo Chen, Huazhong Univ. of Science and Technology (China)
He Tang, Huazhong Univ. of Science and Technology (China)
Zehua Lyu, Huazhong Univ. of Science and Technology (China)
Hu Liang, Huazhong Univ. of Science and Technology (China)
Jun Shang, Huazhong Univ. of Science and Technology (China)
Mudar Serem, Huazhong Univ. of Science and Technology (China)

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