23 October 2014 Saliency modeling via outlier detection
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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.
© 2014 SPIE and IS&T
Chuanbo Chen, Chuanbo Chen, He Tang, He Tang, Zehua Lyu, Zehua Lyu, Hu Liang, Hu Liang, Jun Shang, Jun Shang, Mudar Serem, 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 . Submission:


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