Removing label ambiguity in learning-based visual saliency estimation.

Abstract

Visual saliency is a useful clue to depict visually important image/video contents in many multimedia applications. In visual saliency estimation, a feasible solution is to learn a "feature-saliency" mapping model from the user data obtained by manually labeling activities or eye-tracking devices. However, label ambiguities may also arise due to the inaccurate and inadequate user data. To process the noisy training data, we propose a multi-instance learning to rank approach for visual saliency estimation. In our approach, the correlations between various image patches are incorporated into an ordinal regression framework. By iteratively refining a ranking model and relabeling the image patches with respect to their mutual correlations, the label ambiguities can be effectively removed from the training data. Consequently, visual saliency can be effectively estimated by the ranking model, which can pop out real targets and suppress real distractors. Extensive experiments on two public image data sets show that our approach outperforms 11 state-of-the-art methods remarkably in visual saliency estimation.