Research On Image Saliency Target Detection Algorithms

With the widespread utility and escalation of mobile electronic devices,the use of images to record or express information has become a norm.In order to quickly extract valuable information from massive images,we need to simulate the human visual system to study computer vision hotspots in machine vision systems.Image saliency target detection is to detect the most attractive and most representative part of the image.In the image saliency target detection task,the conventional method generally performs bottom-up and data-driven detection using low-level visual information such as texture,color,etc.For natural scene images with a single target or high contrast,the saliency information can be explored from multiple angles,such as prior knowledge,error reconstruction,etc.However,for the challenging natural scene images,such as complex backgrounds,low contrast,etc.,traditional methods often fail to detect.Deep convolutional neural-network based algorithms take advantage of high-level semantic information combined with context to fully exploit potential details,and have achieved superior saliency detection performance compared to traditional methods.This paper proposes the corresponding methods for the main problems existed in image saliency target detection task.The main contributions of this paper are as follows:In order to fully exploit the various saliency information of the image and make it achieve the complementary effect,this paper proposes a saliency target detection model that combines the reconstruction and priori.Reconstruction includes dense reconstruction and sparse reconstruction.The advantage of dense reconstruction is to make more accurate detection when the salient targets appear at the edges of the image.Sparse reconstruction is more robust and can suppress the complex backgrounds more effectively.The priori includes the background priori and central priori,and the priori knowledge can highlight the salient targets more uniformly.Finally,the saliency maps obtained by the reconstruction and the priori are nonlinearly fused.The experimental results prove the efficiency and superiority of the proposed work in the saliency target detection task.For the problem of multiple saliency targets existed in the image or that of the boundary ambiguity of the detected saliency targets,this paper proposes a deep saliency target detection model based on multi-level continuous features and hierarchical refinement.The model consists of three structures:multi-level continuous feature extraction,hierarchical boundary refinement,and saliency feature fusion.At first,high-level semantic features are continuously extracted and encoded at multiple levels.The process fully exploits global spatial information and details of different levels.Then,the deconvolution operation is used to perform boundary refinement on the multi-level features.Finally,the saliency feature maps obtained are merged to generate the final saliency map.The performance evaluation is carried out using comprehensive metrics on several challenging datasets.The experimental results demonstrate that the method has superior performance.For the problems of low contrast or small targets,this paper proposes a deep saliency target detection model based on channel-wise feature response.The model consists of three parts:channel-wise coarse feature extraction,hierarchical channel-wise feature refinement,and hierarchical feature map fusion.The method is based on the squeeze and excitation residual network,which is modeled based on the correlation between the convolution feature channels.Firstly,channel-wise coarse feature extraction is performed on the input image to generate the global coarse feature maps with more information loss.Then,the channel features are gradually refined from the high level to the low level,and the potential channel correlation details are fully exploited.Finally,the multi-level feature maps are fused to generate the final saliency map.Compared with other state-of-the-art algorithms on multiple saliency datasets with complex scenes,the experimental results demonstrate that the proposed algorithm has high computational efficiency and excellent saliency detection performance.