Deep saliency map estimation of hand-crafted features

Abstract

Saliency detection that utilizes deep convolutional neural networks to obtain high level features from original images has achieved considerable progress during the past years. However, few methods consider learning saliency cues from hand-crafted features. In this paper, we demonstrate that deep learning can produce good enough saliency detection results using only hand-crafted features. We propose a novel multi-context deep learning saliency detection algorithm, where only hand-crafted features are taken into account and modeled in a unified deep learning framework. Extensive experiments on benchmark datasets indicate significant and consistent improvements over the representative deep learning framework based saliency detection methods.