计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 107-112.doi: 10.11896/jsjkx.201100116
刘翔宇1, 蹇木伟1, 鲁祥伟1, 何为凯2, 李晓峰3, 尹义龙3
LIU Xiang-yu1, JIAN Mu-wei1, LU Xiang-wei1, HE Wei-kai2, LI Xiao-feng3, YIN Yi-long3
摘要： 图像显著性检测是计算机视觉中的基础研究课题之一。当前基于深度学习的方法虽然能够有效提高显著性检测结果的准确性,但是在显著性目标的物体边缘细节提取方面还不能令人满意。为此,提出了一种基于眼动点预测先验的边缘细化网络用于显著性目标提取。首先,对输入图像进行眼动点预测,将生成的特征图像作为后续显著性检测的视觉先验;其次,利用多注意力机制VGG16网络进行显著性目标特征提取;最后,对特征图像进行质量优化处理,进一步提升图像显著图的质量。实验结果表明,在3个公开数据集(DUTS,ECSSD,HKU-IS)上,所提方法与其他6个主流方法相比,取得了更好的显著性检测效果。
中图分类号:
| [1] LAURENT I,KOCH C,NIEBUR E.A model of saliency-based visual attention for rapid scene analysis[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2002,20(11):1254-1259. [2] LE MEUR O,LE CALLET P,BARBA D,et al.A coherentcomputational approach to model bottom-up visual attention[J].IEEE TPAMI,2006,28(5):802-817. [3] MATHE S,SMINCHISESCU C.Dynamic eye movement datasets and learnt saliency models for visual action recognition[C]//ECCV.2012. [4] MATHE S,SMINCHISESCU C.Action from still image dataset and inverse optimal control to learn task specific visual scanpaths[C]//NIPS.2013. [5] MATHE S,SMINCHISESCU C.Actions in the eye:Dynamic gaze datasets and learnt saliency models for visual recognition[J].IEEE TPAMI,2015,37(7):1408-1424. [6] ZHANG L,TONG M H,MARKS T K,et al.SUN:A bayesian framework for saliency using natural statistics[J].Journal of Vision,2008,8(7):32-32. [7] GAO D,VASCONCELOS N.Discriminant saliency for visualrecognition from cluttered scenes[C]//NIPS.2005. [8] BRUCE N,TSOTSOS J.Saliency based on information maximization[C]//NIPS.2006. [9] HAREL J,KOCH C,PERONA P.Graph-based visual saliency[C]//NIPS.2007. [10] HOU X,ZHANG L.Saliency detection:A spectral residual approach[C]//CVPR.2007. [11] JUDD T,EHINGER K,DURAND F,et al.Learning to predict where humans look[C]//ICCV.2009. [12] KRUTHIVENTI S,GUDISA V,DHOLAKIYA J,et al.Saliency Unified:A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation[C]//Computer Vision & Pattern Recognition.IEEE,2016. [13] VIG E,DORR M,COX D.Large-scale optimization of hierarchical features for saliency prediction in natural images[C]//CVPR.2014. [14] ZHOU Z,ZHANG L.AFSnet:Fixation Prediction in MovieScenes with Auxiliary Facial Saliency[C]//International Conference on Brain Inspired Cognitive Systems.Cham:Springer,2018. [15] VOLODYMYR M,HEESS N,GRAVES A.Recurrent models of visual attention[C]//Advances in Neural Information Processing Systems.2014. [16] ZHANG X.Progressive Attention Guided Recurrent Networkfor Salient Object Detection[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.IEEE,2018. [17] LIU N,HAN J,YANG M H.PiCANet:Learning Pixel-WiseContextual Attention for Saliency Detection[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2018. [18] WANG W,SHEN J.Deep Visual Attention Prediction[J].IEEE Transactions onImage Processing,2017(99):1-1. [19] CORNIA M.Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model[J].IEEE Transactions on Image Processing,2016(27):5142-5154. [20] DANDAN Z.MAFL:Multi-scale Adversarial Feature Learning for Saliency Detection[C]//2018 International Conference.2018. [21] LINA W.Deep Group-Wise Fully Convolutional Network forCo-Saliency Detection With Graph Propagation[J].IEEE Transactions on Image Processing,2019(99). [22] HECHT-NIELSEN R.Theory of the backpropagation neuralnetwork[J].Neural Networks,1988,1(Supplement-1):445-448. [23] ZAKARIA L,KANNALA J.Context Aware Query Image Representation for Particular Object Retrieval[C]//Scandinavian Conference on Image Analsysi.Cham:Springer,2017:88-99. [24] HU J,SHEN L,SUN G.Squeeze-and-Excitation Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [25] WANG L,LU H,WANG Y,et al.Learning to detect salient objects with image-level supervision[C]//Proc.IEEE Conf.Comput.Vis.Pattern Recognit.(CVPR).2017:136-145. [26] YAN Q,XU L,SHI J,et al.Hierarchical saliency detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:1155-1162. [27] LI G,YU Y.Visual saliency based on multiscale deep features[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:5455-5463. [28] ZHANG P,WANG D,LU H,et al.Amulet:Aggregating multi-level convolutional features for salient object detection[C]//IEEE International Conference on Computer Vision(ICCV 2017).Venice,Italy,2017:202-211. [29] LI G,YU Y.Visual saliency detection based on multiscale deep cnn features[J].IEEE Transactions on Image Processing,2016,25(11):5012-5024. [30] LUO Z,MISHRA A,ACHKAR A,et al.Non-local deep features for salient object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2017:6593-6601. [31] ZHANG P,WANG D,LU H,et al.Learning uncertain convolu-tional features for accurate saliency detection[C]//IEEE Inter-national Conference on Computer Vision (ICCV 2017).Venice, Italy,2017:212-221. [32] LI G,YU Y.Deep contrast learning for salient object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:478-487. [33] LIU N,HAN J.Dhsnet:Deep hierarchical saliency network for salient object detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2016:678-686. [34] SIMONYAN K,ZISSERMAN A.Very deep convolutionalnetworks for large-scale image recognition[J].arXiv:1409.1556,2014. [35] RONNEBERGER O,FISCHER P,BROX T.Unet Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computingand Computer-assisted Intervention.Springer,2015:234-241. [36] ZHAO T,WU X.Pyramid feature attention network for saliency detection[C]//IEEE Conf.Comput.Vis.Pattern Recog..2019:3085-3094. [37] QIN X.BASNet:Boundary-Aware Salient Object Detection[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2019. [38] WANG W.Salient Object Detection Driven by Fixation Prediction[C]//IEEE CVPR.IEEE,2018. |
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