基于深度中心邻域金字塔结构的显著目标检测

doi:10.16451/j.cnki.issn1003-6059.202006003

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Abstract Center-surround based contrast calculation is rarely applied in deep learning-based algorithms. Therefore, a salient object detection method based on deep center-surround pyramid is proposed. Center-surround based contrast and convolutional neural network are combined for salient object detection. Firstly, deep semantic features are introduced into each stage of the network. Then, the dilated convolution is employed to build the center-surround pyramids to capture the contrast information of different scales and generate the corresponding multi-scale conspicuous maps. Finally, all conspicuous maps are further fused to produce final salient object detection result. Comparative experiments on four public datasets verify that the proposed algorithm achieves lower mean average error and higher F measure.

Key words： Salient Object Detection Convolutional Neural Networks Center-Surround Contrast Dilated Convolution

Received: 07 May 2020

ZTFLH:

TP 391.4

Fund:National Natural Science Foundation of China(No.61502358,61502357)

Corresponding Authors: ZHU Lei, Ph.D., associate professor. His research interests include deep learning, salient object detection, object detection and semantic segmentation.

About author:: CHEN Qin, master student. Her research interests include deep learning, salient object detection and semantic segmentation. HOU Yunlong, master student. His research interests include deep learning, salient object detection and semantic segmentation. DENG Huiping, Ph.D., associate profe-ssor. Her research interests include multimedia processing and communication. WU Jin, Ph.D., professor. Her research interests include image processing, pattern recognition, signal processing, multimedia communication, detection technology and automation equipment.

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Cite this article:

CHEN Qin,ZHU Lei,HOU Yunlong等. Salient Object Detection Based on Deep Center-Surround Pyramid[J]. , 2020, 33(6): 496-506.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202006003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I6/496

[1] CHEN T, CHENG M M, TAN P, et al. Sketch2photo: Internet Image Montage. ACM Transactions on Graphics, 2009, 28(5). DOI: 10.1145/1661412.1618470.
[2] WANG L, HUA G, SUKTHANKAR R, et al. Video Object Disco-very and Co-segmentation with Extremely Weak Supervision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(10): 2074-2088.
[3] REN Z X, GAO S H, CHIA L T, et al. Region-Based Saliency Detection and Its Application in Object Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(5): 769-779.
[4] ITTI L, KOCH C, NIEBUR E. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
[5] TREISMAN A M, GELADE G. A Feature-Integration Theory of Attention. Cognitive Psychology, 1980, 12(1): 97-136.
[6] WALTHER D, KOCH C. Modeling Attention to Salient Proto-Objects. Neural Networks, 2006, 19(9): 1395-1407.
[7] GAO D S, VASCONCELOS N. Bottom-Up Saliency Is a Discriminant Process // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2007. DOI: 10.1109/ICCV.2007.4408851.
[8] GARCIA-DIAZ A, FDEZ-VIDAL X R, PARDO X M, et al. Saliency from Hierarchical Adaptation through Decorrelation and Variance Normalization. Image and Vision Computing, 2012, 30(1): 51-64.
[9] LIU T, YUAN Z J, SUN J, et al. Learning to Detect a Salient Object. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 353-367.
[10] JIANG H Z, WANG J D, YUAN Z J, et al. Salient Object Detection: A Discriminative Regional Feature Integration Approach // Proc of the IEEE Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2013: 2083-2090.
[11] LU S, MAHADEVAN V, VASCONCELOS N. Learning Optimal Seeds for Diffusion-Based Salient Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 2790-2797.
[12] 李庆忠,李宜兵,牛炯.基于改进YOLO和迁移学习的水下鱼类目标实时检测.模式识别与人工智能, 2019, 32(3): 193-203.
(LI Q Z, LI Y B, NIU J. Real-Time Detection of Underwater Fish Based on Improved YOLO and Transfer Learning. Pattern Recognition and Artificial Intelligence, 2019, 32(3): 193-203.)
[13] 蒋斌,涂文轩,杨超,等.基于DenseNet的复杂交通场景语义分割方法.模式识别与人工智能, 2019, 32(5): 472-480.
(JIANG B, TU W X, YANG C, et al. Semantic Segmentation Method for Complex Traffic Scene Based on DenseNet. Pattern Recognition and Artificial Intelligence, 2019, 32(5): 472-480.)
[14] HE S F, LAU R W H, LIU W X, et al. SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection. International Journal of Computer Vision, 2015, 115(3): 330-344.
[15] KUEN J, WANG Z H, WANG G. Recurrent Attentional Networks for Saliency Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 3668-3677.
[16] WANG L Z, WANG L J, LU H C, et al. Saliency Detection with Recurrent Fully Convolutional Networks // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 825-841.
[17] LI G B, YU Y Z. Visual Saliency Based on Multiscale Deep Features // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2015: 5455-5463.
[18] LI X, ZHAO L M, WEI L N, et al. DeepSaliency: Multi-task Deep Neural Network Model for Salient Object Detection. IEEE Transactions on Image Processing, 2016, 25(8): 3919-3930.
[19] ZHAO R, OUYANG W L, LI H S, et al. Saliency Detection by Multi-context Deep Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 1265-1274.
[20] WANG L J, LU H C, RUAN X, et al. Deep Networks for Saliency Detection via Local Estimation and Global Search // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 3183-3192.
[21] LEE G Y, TAI Y W, KIM J. Deep Saliency with Encoded Low Level Distance Map and High Level Features // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 660-668.
[22] FRINTROP S, WERNER T, MARTIN-GARCIA G. Traditional Saliency Reloaded: A Good Old Model in New Shape // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 82-90.
[23] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature Pyramid Networks for Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 936-944.
[24] ZHAO H S, SHI J P, QI X J, et al. Pyramid Scene Parsing Network // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2017: 6230-6239.
[25] LIN G S, MILAN A, SHEN C H, et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 1925-1934.
[26] YU F, KOLTUN V. Multi-scale Context Aggregation by Dilated Convolutions[C/OL]. [2020-04-23]. https://arxiv.org/pdf/1511.07122.pdf.
[27] MOVAHEDI V, ELDER J H. Design and Perceptual Validation of Performance Measures for Salient Object Segmentation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 49-56.
[28] WANG L J, LU H C, WANG Y F, et al. Learning to Detect Salient Objects with Image-Level Supervision // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 3796-3805.
[29] YANG C, ZHANG L H, LU H C, et al. Saliency Detection via Graph-Based Manifold Ranking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 3166-3173.
[30] LI Y, HOU X D, KOCH C, et al. The Secrets of Salient Object Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 280-287.
[31] ACHANTA R, HEMAMI R, ESTRADA F, et al. Frequency-Tuned Salient Region Detection // Proc of IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 1597-1604.
[32] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778.
[33] HOU Q B, CHENG M M, HU X W, et al. Deeply Supervised Salient Object Detection with Short Connections. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4): 815-828.
[34] LIU N, HAN J W, YANG M H. PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection[C/OL]. [2020-04-23]. https://arxiv.org/pdf/1708.06433v2.pdf.
[35] WU Z, SU L, HUANG Q M. Cascaded Partial Decoder for Fast and Accurate Salient Object Detection // Proc of the IEEE Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3902-3911.
[36] QIN X B, ZHANG Z C, HUANG C Y, et al. BASNet: Boundary-Aware Salient Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 7479-7489.
[37] LIU J J, HOU Q B, CHENG M M, et al. A Simple Pooling-Based Design for Real-Time Salient Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3912-3921.
[38] XIE S N, TU Z W. Holistically-Nested Edge Detection // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1395-1403.
[39] FU J, LIU J, TIAN H J, et al. Dual Attention Network for Scene Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 3141-3149.