Research On Image Saliency Detection Method Via Structural Feature Enhancement

With the development of modern science and technology,internet,digitalization and intelligence are applied to various industries and gradually permeate into people to live aspect,influencing the life style and values.Through the progress of technology,everything seems to be getting faster,more efficient,more compact and hands-free in many industries.Machines have replaced the traditional complex and repetitive work which requires a lot of manpower can be well completed with the help of machines in saome industries.As an important machine perception,computer vision plays a vital role in many tasks.Computer vision simulates the human visual perception,the scene sematic information can be obtained by processing images,video and multimedia data.Computer vision is like a machine's visual system,which enables the machine to perceive the environment,and providing convenience for human observation and decision-making.One of the key tasks of how computer vision simulates human visual system and enables machines to observe and understand pictures is saliency detection.Human can find a small amount of important information in rich visual scenes quickly,image saliency detection plays the same role in computer vision.At present,most of the methods of saliency detection are based on full convolution depth neural network.Full convolution networks extract contest semantic features by stacking numbers of convolution layers and pooling layers.Pooling layer can enlarge the receptive field and aggregate the semantic context,and it also causes lack of spatial structure information.In some pixel-by-pixel detection tasks,such as image saliency detection,the output image required to be the same size as the original image,the spatial structure information of the image is important to be preserved.For solving the problem in saliency detection,this paper proposes some methods to enhance spatial structure information of saliency detection.The first one is image saliency detection with low-level features enhancement,the method introduces edge map and color block information into network learning.The spatial structure information of bottom features is enhanced by multi-task learning method.Then,the bottom features with strong spatial information are integrated into the high-level features by short connection,and saliency detection that can maintain edge information.The second method,pyramid feature attention network for saliency detection considers about different characteristics of different level features.The channel attention and spatial attention mechanism are used to select the appropriate semantic feature map and filter the background noise of the low level feature,so that the features learnt by the network can focus on the correct saliency region and the boundary between saliency region and background.The last one is weakly supervised image saliency detection via classification model and guided filter optimization.A classificationed model of classification is used to obtain the pseudo-label of saliency detection by threshold restriction.Then the pseudo-label is used to train a saliency detection model.Because the saliency map obtained by using pseudo-labels is inaccurate,we use guided filter to post-process the saliency maps and get the final result.