Saliency detection based on feature learning using Deep Boltzmann Machines

Saliency detection has been a very active research area in recent years. Most traditional methods suffer from the problem that existing visual features are not discriminative or not robust enough to predict salient locations. As a result, the experimental results of these previous methods are still far from satisfactory. In this paper, we propose to utilize a two-layer Deep Boltzmann Machine (DBM) to learn enhanced features from existing contrast-based low-level features, which are more discriminative and reliable. A saliency computation model is then trained to build a mapping from those enhanced features to eye fixation data. The proposed work is amongst the earliest efforts of examining the feasibility of applying deep learning algorithms to saliency detection. Comprehensive evaluations on two publically available benchmark datasets and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness of the proposed work.