Visual saliency detection via invariant feature constrained stacked denoising autoencoder
Visual saliency detection is usually regarded as an image pre-processing method to predict and locate the position and shape of saliency regions. However, many existing saliency detection methods can only obtain the local or even incorrect position and shape of saliency regions, resulting in incompl...
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| Published in: | Multimedia tools and applications Vol. 82; no. 18; pp. 27451 - 27472 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.07.2023
Springer Nature B.V |
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| ISSN: | 1380-7501, 1573-7721 |
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| Abstract | Visual saliency detection is usually regarded as an image pre-processing method to predict and locate the position and shape of saliency regions. However, many existing saliency detection methods can only obtain the local or even incorrect position and shape of saliency regions, resulting in incomplete detection and segmentation of the salient target region. In order to solve this problem, a visual saliency detection method based on scale invariant feature and stacked denoising autoencoder is proposed. Firstly, the deep belief network would be pretrained to initialize the parameters of stacked denoising autoencoder network. Secondly, different from traditional features, scale invariant feature is not limited to the size, resolution, and content of original images. At the same time, it can help the network to restore important features of original images more accurately in multi-scale space. So, scale invariant feature is adopted to design the loss function of the network to complete self-training and update the parameters. Finally, the difference between the final reconstructed image obtained by stacked denoising autoencoder and the original is regarded as the final saliency map. In the experiment, we test the performance of the proposed method in both saliency prediction and saliency object segmentation. The experimental results show that the proposed method has good ability in saliency prediction and has the best performance in saliency object segmentation than other comparison saliency prediction methods and saliency object detection methods. |
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| AbstractList | Visual saliency detection is usually regarded as an image pre-processing method to predict and locate the position and shape of saliency regions. However, many existing saliency detection methods can only obtain the local or even incorrect position and shape of saliency regions, resulting in incomplete detection and segmentation of the salient target region. In order to solve this problem, a visual saliency detection method based on scale invariant feature and stacked denoising autoencoder is proposed. Firstly, the deep belief network would be pretrained to initialize the parameters of stacked denoising autoencoder network. Secondly, different from traditional features, scale invariant feature is not limited to the size, resolution, and content of original images. At the same time, it can help the network to restore important features of original images more accurately in multi-scale space. So, scale invariant feature is adopted to design the loss function of the network to complete self-training and update the parameters. Finally, the difference between the final reconstructed image obtained by stacked denoising autoencoder and the original is regarded as the final saliency map. In the experiment, we test the performance of the proposed method in both saliency prediction and saliency object segmentation. The experimental results show that the proposed method has good ability in saliency prediction and has the best performance in saliency object segmentation than other comparison saliency prediction methods and saliency object detection methods. |
| Author | Xu, Chang Ma, Yunpeng Zhou, Yaqin Yu, Dabing Yu, Zhihong |
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| Cites_doi | 10.1162/neco.2008.04-07-510 10.1109/TMM.2019.2947352 10.1109/TMM.2016.2576283 10.1002/asi.10242 10.1109/CVPR.2016.399 10.1109/CVPR.2019.00320 10.7551/mitpress/7503.003.0073 10.1109/TCSVT.2013.2280096 10.1109/TIP.2018.2882156 10.1007/s11042-015-3037-z 10.1016/j.patcog.2012.02.009 10.1109/TIP.2015.2411433 10.1109/CVPR.2015.7298731 10.1023/B:VISI.0000029664.99615.94 10.1109/TMM.2018.2864613 10.1109/CVPR.2014.360 10.1145/1180639.1180824 10.1109/TMM.2017.2713982 10.1109/TIP.2017.2669878 10.1167/7.9.950 10.1109/TPAMI.2011.146 10.1109/ICCV.2013.370 10.1109/CVPR.2013.151 10.1109/TIP.2012.2199502 10.1109/CVPRW.2012.6239191 10.1109/CVPR.2015.7298935 10.1007/978-3-319-54407-6_19 10.1167/13.4.11 10.1109/ICCV.2017.31 10.1007/s11042-019-7462-2 10.1109/TMM.2017.2693022 10.1007/s11042-019-7423-9 10.1007/s11042-019-7431-9 10.1109/CVPR.2010.5539929 10.1109/LSP.2013.2260737 10.1109/TIP.2015.2440174 10.1109/TIP.2015.2487833 10.1016/j.cviu.2008.08.006 10.1162/neco.2006.18.7.1527 10.1023/A:1026543900054 10.1109/TMM.2015.2389616 10.1109/ICCV.2009.5459462 10.1109/CVPR.2012.6247711 10.1109/CVPR.2006.95 10.1109/TNNLS.2016.2522440 10.1109/TNNLS.2015.2512898 10.1109/TPAMI.2012.98 10.1109/CVPR.2018.00326 10.1109/TMM.2017.2694219 10.1016/j.patcog.2013.03.006 10.1016/j.imavis.2020.103887 10.1109/CVPR.2015.7298938 10.1109/TMM.2016.2638207 10.1109/TMM.2011.2169775 |
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| Keywords | Visual saliency detection Scale invariant feature Saliency prediction Saliency object segmentation Stacked denoising autoencoder Reconstruction network |
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| References | HouXHarelJKochCImage signature: highlighting sparse salient regionsIEEE Trans Pattern Anal Mach Intell20123419420110.1109/TPAMI.2011.146 ErdemEErdemAVisual saliency estimation by nonlinearly integrating features using region covariancesJ Vis2013131110.1167/13.4.11 RahtuEKannalaJSaloMHeikkilaJSegmenting salient objects from images and videos. In: computer vision - ECCV 20102010Heraklion, Crete, GreeceP.V. Springer366379 ChangH-HShihTKChangCKTavanapongWCMAIR: content and mask-aware image retargetingMultimed Tools Appl201978217312175810.1007/s11042-019-7462-2 Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 2376–2383. https://doi.org/10.1109/CVPR.2010.5539929 Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 2814–2821 He J, Feng J, Liu X, et al (2012) Mobile product search with bag of hash bits and boundary reranking. In: 2012 IEEE conference on computer vision and pattern recognition. pp. 3005–3012 Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp 2106–2113 QianXWangHZhaoYHouXHongRWangMTangYYImage location inference by multisaliency enhancementIEEE Trans Multimed20171981382110.1109/TMM.2016.2638207 MahadevanVVasconcelosNBiologically inspired object tracking using center-surround saliency mechanismsIEEE Trans Pattern Anal Mach Intell20133554155410.1109/TPAMI.2012.98 ZhouHYuanYShiCObject tracking using SIFT features and mean shiftComput Vis Image Underst200911334535210.1016/j.cviu.2008.08.006 YeLLiuZLiLShenLBaiCWangYSalient object segmentation via effective integration of saliency and ObjectnessIEEE Trans Multimed2017191742175610.1109/TMM.2017.2693022 HuangFQiJLuHZhangLRuanXSalient object detection via multiple instance learningIEEE Trans Image Process20172619111922363624010.1109/TIP.2017.26698781409.94235 YangCZhangLLuHGraph-regularized saliency detection with convex-Hull-based center priorIEEE Signal Process Lett20132063764010.1109/LSP.2013.2260737 MaCMiaoZZhangXLiMA saliency prior context model for real-time object trackingIEEE Trans Multimed2017192415242410.1109/TMM.2017.2694219 Tavakoli HR, Laaksonen J (2017) Bottom-up fixation prediction using unsupervised hierarchical models. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 287–302 Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. IEEE, pp 506–513 Borji A, Itti L (2012) Exploiting local and global patch rarities for saliency detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 478–485. https://doi.org/10.1109/CVPR.2012.6247711 BorjiAChengMJiangHLiJSalient object detection: a benchmarkIEEE Trans Image Process20152457065722341785210.1109/TIP.2015.24878331408.94882 LoweDGDistinctive image features from scale-invariant keypointsInt J Comput Vis2004609111010.1023/B:VISI.0000029664.99615.94 Kuen J, Wang Z, Wang G (2016) Recurrent Attentional Networks for Saliency Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3668–3677 XiaCQiFShiGBottom–up visual saliency estimation with deep autoencoder-based sparse reconstructionIEEE Trans Neural Netw Learn Syst20162712271240350723610.1109/TNNLS.2015.2512898 Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3080–3089 Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1139–1146 ChengHZhangJWuQAnPA computational model for stereoscopic visual saliency predictionIEEE Trans Multimed20192167868910.1109/TMM.2018.2864613 GaoYWangMTaoDJiRDaiQ3-D object retrieval and recognition with hypergraph analysisIEEE Trans Image Process20122142904303297241810.1109/TIP.2012.21995021373.94131 ZhaiYShahMShahPMVisual attention detection in video sequences using spatiotemporal cuesIn: Proceedings of the 14th annual ACM international conference on Multimedia2006Santa BarbaraACM Press81582410.1145/1180639.1180824 AytekinCPosseggerHMauthnerTKiranyazSBischofHGabboujMSpatiotemporal saliency estimation by spectral foreground detectionIEEE Trans Multimed201820829510.1109/TMM.2017.2713982 LiHLuHLinZShenXPriceBInner and inter label propagation: salient object detection in the wildIEEE Trans Image Process20152431763186335880710.1109/TIP.2015.24401741408.94371 VincentPLarochelleHLajoieIStacked Denoising autoencoders: learning useful representations in a deep network with a local Denoising criterionJ Mach Learn Res2010113371340827561881242.68256 Zhang P, Wang D, Lu H et al (2017) Amulet: aggregating multi-level convolutional features for salient object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 202–211 XiaoSLiTWangJOptimization methods of video images processing for mobile object recognitionMultimed Tools Appl202079172451725510.1007/s11042-019-7423-9 XiaoXZhouYGongYRGB-‘D’ saliency detection with Pseudo depthIEEE Trans Image Process20192821262139390909810.1109/TIP.2018.2882156 Le RouxNBengioYRepresentational power of restricted Boltzmann machines and deep belief networksNeural Comput20082016311649241037010.1162/neco.2008.04-07-5101140.68057 FangSLiJTianYHuangTChenXLearning discriminative subspaces on random contrasts for image saliency analysisIEEE Trans Neural Netw Learn Syst2017281095110810.1109/TNNLS.2016.2522440 RiazSParkULeeS-WA photograph reconstruction by object retargeting for better compositionMultimed Tools Appl201675164391646010.1007/s11042-015-3037-z Borji A, Frintrop S, Sihite DN, Itti L (2012) Adaptive object tracking by learning background context. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp 23–30. https://doi.org/10.1109/CVPRW.2012.6239191 Li X, Lu H, Zhang L et al (2013) Saliency detection via dense and sparse reconstruction. In: 2013 IEEE International Conference on Computer Vision. IEEE, pp 2976–2983 Rafiee, G., Woo, et al (2013) Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches. Pattern Recognit J Pattern Recognit Soc 46:2685–2699 AfshariradHSeyedinSACorrection to: salient object detection using the phase information and object modelMultimed Tools Appl2019781908110.1007/s11042-019-7431-9 DuanLWuCMiaoJVisual saliency detection by spatially weighted dissimilarityCVPR20112011473480 Liu N, Han J, Yang M-H (2018) PiCANet: learning pixel-wise contextual attention for saliency detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp 3089–3098 Wrede, B., Tscherepanow, et al (2012) A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recognit J Pattern Recognit Soc 45:3114–3124 JerripothulaKRCaiJYuanJImage co-segmentation via saliency co-fusionIEEE Trans Multimed2016181896190910.1109/TMM.2016.2576283 BruceNTsotsosJAttention based on information maximizationJ Vis2010795010.1167/7.9.950 DuncanJHumphreysGWVisual search and stimulus similarityJ Am Soc Inf Sci Technol198996433458 FangYLinWLeeBBottom-up saliency detection model based on human visual sensitivity and amplitude SpectrumIEEE Trans Multimed20121418719810.1109/TMM.2011.2169775 Wang L, Lu H, Ruan X, Yang M-H (2015) Deep networks for saliency detection via local estimation and global search. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3183–3192 Abdel-Hakim AE, Farag AA (2006) CSIFT: a SIFT descriptor with color invariant characteristics. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR’06). IEEE, pp 1978–1983 YangXQianXXueYScalable Mobile image retrieval by exploring contextual saliencyIEEE Trans Image Process20152417091721332592710.1109/TIP.2015.24114331408.94764 Bruce NDB, Tsotsos JK (2005) Saliency based on information maximization. In: Advances in Neural Information Processing Systems. pp 155–162 Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: A neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3156–3164 Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 1265–1274 LiuFShenTLouSHanBDeep network saliency detection based on global model and local optimizationActa Opt Sin201737272280 YangSLinGJiangQLinWA dilated inception network for visual saliency predictionIEEE Trans Multimed2020222163217610.1109/TMM.2019.2947352 HintonGEOsinderoSTehYA fast learning algorithm for deep belief netsNeural Comput20061815271554222448510.1162/neco.2006.18.7.15271106.68094 AhlgrenPJarnevingBRousseauRRequirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficientJ Am Soc Inf Sci Technol20035455056010.1002/asi.10242 GaoYShiMTaoDXuCDatabase saliency for fast image retrievalIEEE Trans Multimed20151735936910.1109/TMM.2015.2389616 RubnerYTomasiCGuibasLJThe earth Mover’s distance as a metric for image retrievalInt J Comput Vis2000409912110.1023/A:10265439000541012.68705 JiaSBruceNDBEML-NET: An expandable multi-layer NETwork for saliency predictionImage Vis Comput20209510388710.1016/j.imavis.2020.103887 Kim K-S, Yoon Y-J, Kang M-C et al (2014) An improved GrabCut using a saliency map. In: 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE). IEEE, pp 317–318 RenZGaoSChiaL-TTsangIW-HRegion-based sa 14525_CR30 X Hou (14525_CR24) 2012; 34 14525_CR31 F Liu (14525_CR35) 2017; 37 X Yang (14525_CR56) 2015; 24 Y Zhai (14525_CR59) 2006 C Xia (14525_CR52) 2016; 27 14525_CR36 Y Rubner (14525_CR46) 2000; 40 H Afsharirad (14525_CR2) 2019; 78 14525_CR33 C Yang (14525_CR55) 2013; 20 KR Jerripothula (14525_CR26) 2016; 18 14525_CR29 L Duan (14525_CR13) 2011; 2011 H-H Chang (14525_CR10) 2019; 78 N Bruce (14525_CR9) 2010; 7 14525_CR42 S Jia (14525_CR27) 2020; 95 14525_CR40 14525_CR49 14525_CR47 Z Ren (14525_CR44) 2014; 24 L Ye (14525_CR58) 2017; 19 Y Fang (14525_CR16) 2012; 14 J Duncan (14525_CR14) 1989; 96 S Fang (14525_CR17) 2017; 28 DG Lowe (14525_CR37) 2004; 60 S Riaz (14525_CR45) 2016; 75 14525_CR50 V Mahadevan (14525_CR39) 2013; 35 14525_CR51 S Yang (14525_CR57) 2020; 22 S Xiao (14525_CR54) 2020; 79 C Aytekin (14525_CR4) 2018; 20 A Borji (14525_CR7) 2015; 24 E Rahtu (14525_CR43) 2010 P Vincent (14525_CR48) 2010; 11 X Qian (14525_CR41) 2017; 19 X Xiao (14525_CR53) 2019; 28 Y Gao (14525_CR18) 2012; 21 M Cheng (14525_CR11) 2011; 2011 GE Hinton (14525_CR23) 2006; 18 14525_CR20 14525_CR64 14525_CR61 14525_CR62 14525_CR60 14525_CR28 Y Gao (14525_CR19) 2015; 17 14525_CR21 14525_CR22 14525_CR1 E Erdem (14525_CR15) 2013; 13 14525_CR8 H Li (14525_CR34) 2015; 24 14525_CR5 14525_CR6 C Ma (14525_CR38) 2017; 19 P Ahlgren (14525_CR3) 2003; 54 N Le Roux (14525_CR32) 2008; 20 H Cheng (14525_CR12) 2019; 21 F Huang (14525_CR25) 2017; 26 H Zhou (14525_CR63) 2009; 113 |
| References_xml | – reference: JiaSBruceNDBEML-NET: An expandable multi-layer NETwork for saliency predictionImage Vis Comput20209510388710.1016/j.imavis.2020.103887 – reference: BruceNTsotsosJAttention based on information maximizationJ Vis2010795010.1167/7.9.950 – reference: JerripothulaKRCaiJYuanJImage co-segmentation via saliency co-fusionIEEE Trans Multimed2016181896190910.1109/TMM.2016.2576283 – reference: RahtuEKannalaJSaloMHeikkilaJSegmenting salient objects from images and videos. In: computer vision - ECCV 20102010Heraklion, Crete, GreeceP.V. Springer366379 – reference: He J, Feng J, Liu X, et al (2012) Mobile product search with bag of hash bits and boundary reranking. In: 2012 IEEE conference on computer vision and pattern recognition. pp. 3005–3012 – reference: Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 1265–1274 – reference: RenZGaoSChiaL-TTsangIW-HRegion-based saliency detection and its application in object recognitionIEEE Trans Circuits Syst Video Technol20142476977910.1109/TCSVT.2013.2280096 – reference: BorjiAChengMJiangHLiJSalient object detection: a benchmarkIEEE Trans Image Process20152457065722341785210.1109/TIP.2015.24878331408.94882 – reference: RiazSParkULeeS-WA photograph reconstruction by object retargeting for better compositionMultimed Tools Appl201675164391646010.1007/s11042-015-3037-z – reference: GaoYWangMTaoDJiRDaiQ3-D object retrieval and recognition with hypergraph analysisIEEE Trans Image Process20122142904303297241810.1109/TIP.2012.21995021373.94131 – reference: Bruce NDB, Tsotsos JK (2005) Saliency based on information maximization. In: Advances in Neural Information Processing Systems. pp 155–162 – reference: HuangFQiJLuHZhangLRuanXSalient object detection via multiple instance learningIEEE Trans Image Process20172619111922363624010.1109/TIP.2017.26698781409.94235 – reference: ChengHZhangJWuQAnPA computational model for stereoscopic visual saliency predictionIEEE Trans Multimed20192167868910.1109/TMM.2018.2864613 – reference: FangYLinWLeeBBottom-up saliency detection model based on human visual sensitivity and amplitude SpectrumIEEE Trans Multimed20121418719810.1109/TMM.2011.2169775 – reference: Liu N, Han J, Yang M-H (2018) PiCANet: learning pixel-wise contextual attention for saliency detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp 3089–3098 – reference: YangCZhangLLuHGraph-regularized saliency detection with convex-Hull-based center priorIEEE Signal Process Lett20132063764010.1109/LSP.2013.2260737 – reference: Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3080–3089 – reference: AfshariradHSeyedinSACorrection to: salient object detection using the phase information and object modelMultimed Tools Appl2019781908110.1007/s11042-019-7431-9 – reference: AhlgrenPJarnevingBRousseauRRequirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficientJ Am Soc Inf Sci Technol20035455056010.1002/asi.10242 – reference: HintonGEOsinderoSTehYA fast learning algorithm for deep belief netsNeural Comput20061815271554222448510.1162/neco.2006.18.7.15271106.68094 – reference: Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1139–1146 – reference: ChangH-HShihTKChangCKTavanapongWCMAIR: content and mask-aware image retargetingMultimed Tools Appl201978217312175810.1007/s11042-019-7462-2 – reference: GaoYShiMTaoDXuCDatabase saliency for fast image retrievalIEEE Trans Multimed20151735936910.1109/TMM.2015.2389616 – reference: LiHLuHLinZShenXPriceBInner and inter label propagation: salient object detection in the wildIEEE Trans Image Process20152431763186335880710.1109/TIP.2015.24401741408.94371 – reference: XiaCQiFShiGBottom–up visual saliency estimation with deep autoencoder-based sparse reconstructionIEEE Trans Neural Netw Learn Syst20162712271240350723610.1109/TNNLS.2015.2512898 – reference: Zhang P, Wang D, Lu H et al (2017) Amulet: aggregating multi-level convolutional features for salient object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 202–211 – reference: LiuFShenTLouSHanBDeep network saliency detection based on global model and local optimizationActa Opt Sin201737272280 – reference: LoweDGDistinctive image features from scale-invariant keypointsInt J Comput Vis2004609111010.1023/B:VISI.0000029664.99615.94 – reference: Borji A, Itti L (2012) Exploiting local and global patch rarities for saliency detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 478–485. https://doi.org/10.1109/CVPR.2012.6247711 – reference: Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in Neural Information Processing Systems 19. The MIT Press, pp 545–552. https://doi.org/10.7551/mitpress/7503.003.0073 – reference: Wang L, Lu H, Ruan X, Yang M-H (2015) Deep networks for saliency detection via local estimation and global search. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3183–3192 – reference: MaCMiaoZZhangXLiMA saliency prior context model for real-time object trackingIEEE Trans Multimed2017192415242410.1109/TMM.2017.2694219 – reference: ErdemEErdemAVisual saliency estimation by nonlinearly integrating features using region covariancesJ Vis2013131110.1167/13.4.11 – reference: Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. IEEE, pp 506–513 – reference: DuncanJHumphreysGWVisual search and stimulus similarityJ Am Soc Inf Sci Technol198996433458 – reference: Borji A, Frintrop S, Sihite DN, Itti L (2012) Adaptive object tracking by learning background context. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp 23–30. https://doi.org/10.1109/CVPRW.2012.6239191 – reference: Rafiee, G., Woo, et al (2013) Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches. Pattern Recognit J Pattern Recognit Soc 46:2685–2699 – reference: Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp 2106–2113 – reference: YangSLinGJiangQLinWA dilated inception network for visual saliency predictionIEEE Trans Multimed2020222163217610.1109/TMM.2019.2947352 – reference: Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 2814–2821 – reference: Kuen J, Wang Z, Wang G (2016) Recurrent Attentional Networks for Saliency Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3668–3677 – reference: FangSLiJTianYHuangTChenXLearning discriminative subspaces on random contrasts for image saliency analysisIEEE Trans Neural Netw Learn Syst2017281095110810.1109/TNNLS.2016.2522440 – reference: YangXQianXXueYScalable Mobile image retrieval by exploring contextual saliencyIEEE Trans Image Process20152417091721332592710.1109/TIP.2015.24114331408.94764 – reference: AytekinCPosseggerHMauthnerTKiranyazSBischofHGabboujMSpatiotemporal saliency estimation by spectral foreground detectionIEEE Trans Multimed201820829510.1109/TMM.2017.2713982 – reference: Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 2376–2383. https://doi.org/10.1109/CVPR.2010.5539929 – reference: DuanLWuCMiaoJVisual saliency detection by spatially weighted dissimilarityCVPR20112011473480 – reference: Kim K-S, Yoon Y-J, Kang M-C et al (2014) An improved GrabCut using a saliency map. In: 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE). IEEE, pp 317–318 – reference: Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: A neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3156–3164 – reference: Wrede, B., Tscherepanow, et al (2012) A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recognit J Pattern Recognit Soc 45:3114–3124 – reference: ChengMZhangGMitraNJGlobal contrast based salient region detectionCVPR20112011409416 – reference: RubnerYTomasiCGuibasLJThe earth Mover’s distance as a metric for image retrievalInt J Comput Vis2000409912110.1023/A:10265439000541012.68705 – reference: Tavakoli HR, Laaksonen J (2017) Bottom-up fixation prediction using unsupervised hierarchical models. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 287–302 – reference: HouXHarelJKochCImage signature: highlighting sparse salient regionsIEEE Trans Pattern Anal Mach Intell20123419420110.1109/TPAMI.2011.146 – reference: YeLLiuZLiLShenLBaiCWangYSalient object segmentation via effective integration of saliency and ObjectnessIEEE Trans Multimed2017191742175610.1109/TMM.2017.2693022 – reference: VincentPLarochelleHLajoieIStacked Denoising autoencoders: learning useful representations in a deep network with a local Denoising criterionJ Mach Learn Res2010113371340827561881242.68256 – reference: QianXWangHZhaoYHouXHongRWangMTangYYImage location inference by multisaliency enhancementIEEE Trans Multimed20171981382110.1109/TMM.2016.2638207 – reference: Abdel-Hakim AE, Farag AA (2006) CSIFT: a SIFT descriptor with color invariant characteristics. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR’06). IEEE, pp 1978–1983 – reference: XiaoSLiTWangJOptimization methods of video images processing for mobile object recognitionMultimed Tools Appl202079172451725510.1007/s11042-019-7423-9 – reference: Le RouxNBengioYRepresentational power of restricted Boltzmann machines and deep belief networksNeural Comput20082016311649241037010.1162/neco.2008.04-07-5101140.68057 – reference: MahadevanVVasconcelosNBiologically inspired object tracking using center-surround saliency mechanismsIEEE Trans Pattern Anal Mach Intell20133554155410.1109/TPAMI.2012.98 – reference: Li X, Lu H, Zhang L et al (2013) Saliency detection via dense and sparse reconstruction. In: 2013 IEEE International Conference on Computer Vision. IEEE, pp 2976–2983 – reference: XiaoXZhouYGongYRGB-‘D’ saliency detection with Pseudo depthIEEE Trans Image Process20192821262139390909810.1109/TIP.2018.2882156 – reference: ZhaiYShahMShahPMVisual attention detection in video sequences using spatiotemporal cuesIn: Proceedings of the 14th annual ACM international conference on Multimedia2006Santa BarbaraACM Press81582410.1145/1180639.1180824 – reference: ZhouHYuanYShiCObject tracking using SIFT features and mean shiftComput Vis Image Underst200911334535210.1016/j.cviu.2008.08.006 – volume: 20 start-page: 1631 year: 2008 ident: 14525_CR32 publication-title: Neural Comput doi: 10.1162/neco.2008.04-07-510 – volume: 22 start-page: 2163 year: 2020 ident: 14525_CR57 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2019.2947352 – volume: 18 start-page: 1896 year: 2016 ident: 14525_CR26 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2016.2576283 – volume: 54 start-page: 550 year: 2003 ident: 14525_CR3 publication-title: J Am Soc Inf Sci Technol doi: 10.1002/asi.10242 – ident: 14525_CR31 doi: 10.1109/CVPR.2016.399 – ident: 14525_CR61 doi: 10.1109/CVPR.2019.00320 – volume: 11 start-page: 3371 year: 2010 ident: 14525_CR48 publication-title: J Mach Learn Res – volume: 2011 start-page: 409 year: 2011 ident: 14525_CR11 publication-title: CVPR – ident: 14525_CR21 doi: 10.7551/mitpress/7503.003.0073 – volume: 24 start-page: 769 year: 2014 ident: 14525_CR44 publication-title: IEEE Trans Circuits Syst Video Technol doi: 10.1109/TCSVT.2013.2280096 – start-page: 366 volume-title: Segmenting salient objects from images and videos. In: computer vision - ECCV 2010 year: 2010 ident: 14525_CR43 – volume: 28 start-page: 2126 year: 2019 ident: 14525_CR53 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2018.2882156 – volume: 75 start-page: 16439 year: 2016 ident: 14525_CR45 publication-title: Multimed Tools Appl doi: 10.1007/s11042-015-3037-z – ident: 14525_CR51 doi: 10.1016/j.patcog.2012.02.009 – volume: 24 start-page: 1709 year: 2015 ident: 14525_CR56 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2015.2411433 – ident: 14525_CR62 doi: 10.1109/CVPR.2015.7298731 – volume: 60 start-page: 91 year: 2004 ident: 14525_CR37 publication-title: Int J Comput Vis doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 21 start-page: 678 year: 2019 ident: 14525_CR12 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2018.2864613 – ident: 14525_CR64 doi: 10.1109/CVPR.2014.360 – start-page: 815 volume-title: In: Proceedings of the 14th annual ACM international conference on Multimedia year: 2006 ident: 14525_CR59 doi: 10.1145/1180639.1180824 – volume: 20 start-page: 82 year: 2018 ident: 14525_CR4 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2017.2713982 – volume: 26 start-page: 1911 year: 2017 ident: 14525_CR25 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2017.2669878 – volume: 7 start-page: 950 year: 2010 ident: 14525_CR9 publication-title: J Vis doi: 10.1167/7.9.950 – volume: 34 start-page: 194 year: 2012 ident: 14525_CR24 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2011.146 – ident: 14525_CR33 doi: 10.1109/ICCV.2013.370 – ident: 14525_CR40 doi: 10.1109/CVPR.2013.151 – volume: 21 start-page: 4290 year: 2012 ident: 14525_CR18 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2012.2199502 – ident: 14525_CR6 doi: 10.1109/CVPRW.2012.6239191 – volume: 2011 start-page: 473 year: 2011 ident: 14525_CR13 publication-title: CVPR – ident: 14525_CR49 doi: 10.1109/CVPR.2015.7298935 – ident: 14525_CR47 doi: 10.1007/978-3-319-54407-6_19 – volume: 13 start-page: 11 year: 2013 ident: 14525_CR15 publication-title: J Vis doi: 10.1167/13.4.11 – ident: 14525_CR60 doi: 10.1109/ICCV.2017.31 – volume: 78 start-page: 21731 year: 2019 ident: 14525_CR10 publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-7462-2 – volume: 19 start-page: 1742 year: 2017 ident: 14525_CR58 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2017.2693022 – volume: 96 start-page: 433 year: 1989 ident: 14525_CR14 publication-title: J Am Soc Inf Sci Technol – volume: 79 start-page: 17245 year: 2020 ident: 14525_CR54 publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-7423-9 – volume: 78 start-page: 19081 year: 2019 ident: 14525_CR2 publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-7431-9 – ident: 14525_CR20 doi: 10.1109/CVPR.2010.5539929 – volume: 20 start-page: 637 year: 2013 ident: 14525_CR55 publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2013.2260737 – volume: 24 start-page: 3176 year: 2015 ident: 14525_CR34 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2015.2440174 – volume: 24 start-page: 5706 year: 2015 ident: 14525_CR7 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2015.2487833 – volume: 113 start-page: 345 year: 2009 ident: 14525_CR63 publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2008.08.006 – volume: 18 start-page: 1527 year: 2006 ident: 14525_CR23 publication-title: Neural Comput doi: 10.1162/neco.2006.18.7.1527 – volume: 40 start-page: 99 year: 2000 ident: 14525_CR46 publication-title: Int J Comput Vis doi: 10.1023/A:1026543900054 – ident: 14525_CR29 – ident: 14525_CR30 – volume: 17 start-page: 359 year: 2015 ident: 14525_CR19 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2015.2389616 – ident: 14525_CR28 doi: 10.1109/ICCV.2009.5459462 – ident: 14525_CR5 doi: 10.1109/CVPR.2012.6247711 – ident: 14525_CR1 doi: 10.1109/CVPR.2006.95 – volume: 28 start-page: 1095 year: 2017 ident: 14525_CR17 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2016.2522440 – volume: 27 start-page: 1227 year: 2016 ident: 14525_CR52 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2015.2512898 – volume: 35 start-page: 541 year: 2013 ident: 14525_CR39 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2012.98 – ident: 14525_CR36 doi: 10.1109/CVPR.2018.00326 – volume: 19 start-page: 2415 year: 2017 ident: 14525_CR38 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2017.2694219 – ident: 14525_CR42 doi: 10.1016/j.patcog.2013.03.006 – ident: 14525_CR22 – volume: 37 start-page: 272 year: 2017 ident: 14525_CR35 publication-title: Acta Opt Sin – volume: 95 start-page: 103887 year: 2020 ident: 14525_CR27 publication-title: Image Vis Comput doi: 10.1016/j.imavis.2020.103887 – ident: 14525_CR8 – ident: 14525_CR50 doi: 10.1109/CVPR.2015.7298938 – volume: 19 start-page: 813 year: 2017 ident: 14525_CR41 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2016.2638207 – volume: 14 start-page: 187 year: 2012 ident: 14525_CR16 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2011.2169775 |
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