Research On Some Key Technologies Of Salient Object Detection And Its Applications

When seeing a scenario,we humans,as intelligent beings,often focus our attention on some important objects while unconsciously filter out a lot of unimportant information.These objects that attract our visual attention are called salient objects.Fast and accurate detection of these visually salient objects in images can effectively provide guidance for computer resource allocation,eliminate distraction factors,and make many complex visual tasks easier.Salient objects are of great significance to computer vision and artificial intelligence tasks.However,it is a huge challenge to accurately detect salient objects in a given scenario.First,salient objects have a variety of features that cannot be represented by a simply mathematical model.Second,salient objects have object attributes.Hence,more semantically meaningful features are needed to describe these attributes.Finally,salient object detection often serves as the pre-processing part of other visual tasks.Therefore,salient object detection should be highly efficient.How to equip computers with visual processing attributes similar to humans and how to quickly detect salient objects in a given scenario are an important topic in modern computer vision and artificial intelligence fields.By centering on several key technologies of salient object detection algorithms,this paper researches salient object detection algorithms based on image dense correspondence,analyzes salient object detection algorithms based on fully-supervised deep learning and webly supervised learning,and discusses the application of salient object detection algorithms in various visual tasks.The contributions of this paper are as follows.1.A salient object detection algorithm based on image dense correspondence is proposed.Different from the existing unsupervised algorithms,this algorithm analyzes and explains object saliency in images from a brand new perspective and is able to combine both the intrinsic and extrinsic information.Generally,this algorithm is based on abstract visually salient features and quickly establishes dense correspondence between a given image and the existing annotated images.Using the obtained information,it is able to infer the salient object region of the given image.By considering both the information of external samples and the intrinsic information of the given image,this algorithm can quickly and accurately detect the salient object region in a given image.This algorithm has been tested on sixwidely-used public datasets.The results show that it achieves higher accuracy than the existing unsupervised salient object detection algorithms.2.A salient object detection algorithm based on fully-supervised deep convolutional networks is proposed.The deep neural network is able to learn semantic features in an end-to-end manner.Therefore,it greatly improves the accuracy of salient object detection.This algorithm adopts a novel multi-scale cascaded deep convolutional network and learns the saliency in an end-to-end manner.Different from the existing deep-learning-based saliency object detection algorithms,this algorithm 1)considers multi-scale context information in salient object detection tasks,2)integrates the post processing part into a neural network.This method detects salient objects in a coarse-to-fine manner and constantly adjusts the detection results.The experiments on multiple public datasets show that the proposed algorithm achieves the currently highest accuracy among the existing salient object detection methods.Meanwhile,its operation efficiency is higher than most deep-learning-based object detection algorithms.3.Deep learning requires great numbers of annotated samples to learn the semantic features of images.Therefore,it needs huge amounts of labor,time and capital for data annotation.To solve this problem,this paper proposes a webly supervised salient object detection method.This method utilizes easily accessible images collected from the Internet to train the deep neural network and requires no manual salient object region annotation.Experiments show that this method achieves accuracy comparable to and even higher than fully-supervised deep neural networks.4.This paper discusses the application of object detection algorithms in two computer vision tasks,including region segmentation of sternum X-ray and dense semantic matching.Experiments show that the salient object detection algorithm is able to quickly extract visually silent region,and thus improve computational efficiency and accuracy.