Visual Saliency Based on Multiscale Deep Features

TL;DR

This paper presents a novel deep learning-based approach for visual saliency detection that leverages multiscale CNN features, a refinement process, and multiple segmentation levels, achieving state-of-the-art results and introducing a new large dataset.

Contribution

The paper introduces a multiscale CNN feature-based neural network architecture for saliency detection, along with a refinement method and multi-level segmentation aggregation, plus a new challenging dataset.

Findings

Achieves state-of-the-art performance on public benchmarks.

Improves F-Measure by 5.0% and 13.2% on MSRA-B and HKU-IS datasets.

Reduces mean absolute error by 5.7% and 35.1% on the same datasets.

Abstract

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this CVPR 2015 paper, we discover that a high-quality visual saliency model can be trained with multiscale features extracted using a popular deep learning architecture, convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for extracting features at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotation. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.