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Boosting bottom-up and top-down visual features for saliency estimation

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1 Author(s)
Borji, A. ; Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA

Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency maps of previous best bottom-up models with top-down cognitive visual features (e.g., faces, humans, cars, etc.) and learn a direct mapping from those features to eye fixations using Regression, SVM, and AdaBoost classifiers. By extensive experimenting over three benchmark eye-tracking datasets using three popular evaluation scores, we show that our boosting model outperforms 27 state-of-the-art models and is so far the closest model to the accuracy of human model for fixation prediction. Furthermore, our model successfully detects the most salient object in a scene without sophisticated image processings such as region segmentation.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on

Date of Conference:

16-21 June 2012