Visual saliency computation and quality evaluation via deep learning

Visual attention is an important mechanism in our human vision system, which filters out redundant and unimportant visual information for selectively processing the most salient or informative regions from the visual field. Visual saliency computation is about understanding and simulating the behavior of this selective attention mechanism in a visual scene. Computational models for visual saliency can provide clues to where people will look in images, what objects are salient in a scene, and how people will evaluate the perceptual quality of an image. Such models can be applied to advance a wide range of visual-oriented applications in image processing and computer vision areas. At present, recent advances in visual saliency computation are mainly led by the progress in deep learning techniques and many deep learning-based visual saliency approaches have emerged. In this thesis, we study the problems of deep learning-based visual saliency computation, including saliency prediction and salient object detection (SOD). Besides, saliency-guided image quality evaluation is also investigated to extend our work. For saliency prediction, existing deep saliency models suffer from either huge computation cost or limited performance gain. We propose an effective yet efficient saliency model, named Dilated Inception Network (DINet), to characterize the diverse and effective saliency-influential factors at different receptive field sizes with much smaller computation cost. Experimental results on the challenging saliency prediction datasets demonstrate the outstanding performance of our model in terms of both speed and accuracy. For SOD, the saliency maps produced by previous works still suffer from incomplete predictions due to the internal complexity of salient objects. To alleviate this problem, we propose a simple yet effective progressive self-guided loss function (PSG loss) to create progressive and auxiliary training supervisions for step-wisely guiding the training process. In our PSG loss, a simulated morphological closing operation is applied to the network predictions to generate the needed progressive supervisions epoch-wisely for characterizing the spatial dependencies of salient object pixels. As a result, SOD models can be guided by these generated supervisions to highlight more complete salient objects step-by-step for alleviating the problem of incomplete predictions. Experimental results on six widely used SOD benchmark datasets show that our loss function not only advances the performance of existing SOD models without architecture modification but also helps our proposed framework to achieve state-of-the-art performance. In the last work, we propose a novel saliency-guided deep neural network (SGDNet) to incorporate learnable saliency information into image quality evaluation. This model is the first attempt to jointly optimize the saliency prediction and quality evaluation sub-tasks in an end-to-end multi-task learning framework. The learned saliency information from the saliency prediction sub-task is transparent to the quality evaluation sub-task by providing a kind of spatial attention priors for the perceptually-consistent feature fusion. The effectiveness of the learned saliency information and the proposed multi-task framework are validated in the experiments.Doctor of Philosoph