Visual saliency is a fundamental problem in both cogni tive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural net work architecture, which has fully connected layers on top ofCNNs responsible for feature extraction at three dif f erent scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggre gating multiple saliency maps computed for dif f erent 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 eval uation of visual saliency models, we also construct a new large database of 4447 challenging images and their pix elwise saliency annotations. Experimental results demon strate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, im proving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKV-IS), and lowering the mean absolute error by 5.7% and 35. I % re spectively on these two datasets.
To compensate for these weaknesses, a Multiscale Deep Feature (MDF) model was proposed in REF , with fully connected layers on top of convolutional neural networks for feature extraction at three different scales.
Visual saliency based on multiscale deep features
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science