【图像显著性检测算法研究的国内外文献综述2000字】

图像显著性检测算法研究的国内外文献综述显著性检测已经发展了很多年，在这期间涌现出了大批量的优秀算法，算法的检测性能也在稳步提高当中。早期的显著物体检测研究主要是基于手工制作的特征ADDINEN.CITEADDINEN.CITE.DATA[16-18]，例如颜色、方向、亮度和纹理ADDINEN.CITE<EndNote><Cite><Author>闯跃龙</Author><Year>2020</Year><RecNum>68</RecNum><DisplayText><styleface="superscript">[19]</style></DisplayText><record><rec-number>68</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1618666090">68</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>闯跃龙</author><author>张石清</author><author>赵小明</author></authors></contributors><auth-address>台州学院图像处理与模式识别研究所;</auth-address><titles><title>基于多尺度信息处理和Gabor初始化的图像显著性对象检测</title><secondary-title>光电子·激光</secondary-title></titles><periodical><full-title>光电子·激光</full-title></periodical><pages>834-841</pages><volume>31</volume><number>08</number><keywords><keyword>多尺度信息</keyword><keyword>Gabor</keyword><keyword>显著性对象检测</keyword><keyword>卷积神经网络</keyword></keywords><dates><year>2020</year></dates><isbn>1005-0086</isbn><call-num>12-1182/O4</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[19]等特征来得到局部或者全局信息，IttiADDINEN.CITE<EndNote><Cite><Author>Itti</Author><Year>1998</Year><RecNum>93</RecNum><DisplayText><styleface="superscript">[20]</style></DisplayText><record><rec-number>93</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620262477">93</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>L.Itti</author><author>C.Koch</author><author>E.Niebur</author></authors></contributors><titles><title>Amodelofsaliency-basedvisualattentionforrapidsceneanalysis</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>1254-1259</pages><volume>20</volume><number>11</number><dates><year>1998</year></dates><isbn>1939-3539</isbn><urls></urls><electronic-resource-num>10.1109/34.730558</electronic-resource-num></record></Cite></EndNote>[20]提出了一种基于显著性的视觉注意快速场景分析模型。文章首次提出了视觉注意系统，对后续显著性物体检测的发展起到了很重要的作用，是显著性物体检测发展过程中的一座里程碑。现代的显著物体检测模型主要依赖于卷积神经网络ADDINEN.CITE<EndNote><Cite><Author>Chua</Author><Year>1993</Year><RecNum>97</RecNum><DisplayText><styleface="superscript">[21]</style></DisplayText><record><rec-number>97</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620263203">97</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>L.O.Chua</author><author>T.Roska</author></authors></contributors><titles><title>TheCNNparadigm</title><secondary-title>IEEETransactionsonCircuitsandSystemsI:FundamentalTheoryandApplications</secondary-title></titles><periodical><full-title>IEEETransactionsonCircuitsandSystemsI:FundamentalTheoryandApplications</full-title></periodical><pages>147-156</pages><volume>40</volume><number>3</number><dates><year>1993</year></dates><isbn>1558-1268</isbn><urls></urls><electronic-resource-num>10.1109/81.222795</electronic-resource-num></record></Cite></EndNote>[21]（ConvolutionNeuralNetwork，CNN），Liu等人ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2011</Year><RecNum>94</RecNum><DisplayText><styleface="superscript">[22]</style></DisplayText><record><rec-number>94</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620262854">94</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>T.Liu</author><author>Z.Yuan</author><author>J.Sun</author><author>J.Wang</author><author>N.Zheng</author><author>X.Tang</author><author>H.Shum</author></authors></contributors><titles><title>LearningtoDetectaSalientObject</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>353-367</pages><volume>33</volume><number>2</number><dates><year>2011</year></dates><isbn>1939-3539</isbn><urls></urls><electronic-resource-num>10.1109/TPAMI.2010.70</electronic-resource-num></record></Cite></EndNote>[22]首次将卷积神经网络应用于图像显著物体检测中，其原理是利用卷积提取图像特征，以达到检测显著性区域的目的。在随后的发展中，基于深度学习的显著物体检测模型ADDINEN.CITEADDINEN.CITE.DATA[23-25]越来越多，且逐渐表现出越来越优秀的结果。传统的显著物体检测模型主要依赖于图像的传统特征，比如图像的方向，亮度，纹理等等特征，IttiADDINEN.CITE<EndNote><Cite><Author>Itti</Author><Year>1998</Year><RecNum>93</RecNum><DisplayText><styleface="superscript">[20]</style></DisplayText><record><rec-number>93</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620262477">93</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>L.Itti</author><author>C.Koch</author><author>E.Niebur</author></authors></contributors><titles><title>Amodelofsaliency-basedvisualattentionforrapidsceneanalysis</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>1254-1259</pages><volume>20</volume><number>11</number><dates><year>1998</year></dates><isbn>1939-3539</isbn><urls></urls><electronic-resource-num>10.1109/34.730558</electronic-resource-num></record></Cite></EndNote>[20]提出了基于显著性的视觉注意快速场景分析模型，文章首先利用高斯金字塔对图像进行线性过滤，然后把图像分为亮度、颜色和方向三个层次，分开进行处理，得到颜色、亮度和方向显著图，最后进行线性组合得到最终的显著性图像。Liu等人ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2011</Year><RecNum>94</RecNum><DisplayText><styleface="superscript">[22]</style></DisplayText><record><rec-number>94</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620262854">94</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>T.Liu</author><author>Z.Yuan</author><author>J.Sun</author><author>J.Wang</author><author>N.Zheng</author><author>X.Tang</author><author>H.Shum</author></authors></contributors><titles><title>LearningtoDetectaSalientObject</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>353-367</pages><volume>33</volume><number>2</number><dates><year>2011</year></dates><isbn>1939-3539</isbn><urls></urls><electronic-resource-num>10.1109/TPAMI.2010.70</electronic-resource-num></record></Cite></EndNote>[22]首次将显著物体检测转化为图像分割问题，将显著物体与背景分割开来，计算图像的中心-周边直方图、不同尺度对比图和色彩空间分布等等特征，最后将显著物体与图像背景分开。AchantaR等人提出FT模型ADDINEN.CITE<EndNote><Cite><Author>Achanta</Author><Year>2009</Year><RecNum>95</RecNum><DisplayText><styleface="superscript">[26]</style></DisplayText><record><rec-number>95</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1620262996">95</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>R.Achanta</author><author>S.Hemami</author><author>F.Estrada</author><author>S.Susstrunk</author></authors></contributors><titles><title>Frequency-tunedsalientregiondetection</title><secondary-title>2009IEEEConferenceonComputerVisionandPatternRecognition</secondary-title><alt-title>2009IEEEConferenceonComputerVisionandPatternRecognition</alt-title></titles><pages>1597-1604</pages><dates><year>2009</year><pub-dates><date>20-25June2009</date></pub-dates></dates><isbn>1063-6919</isbn><urls></urls><electronic-resource-num>10.1109/CVPR.2009.5206596</electronic-resource-num></record></Cite></EndNote>[26]来进行显著物体检测，该方法可以得到全分辨率的显著图且具有清晰明了的显著物体边界。通过提取图像的频率信息可以保留原始图像中的边界，模型利用了图像的颜色和亮度的特征，易于实现且计算效率高。程明明ADDINEN.CITE<EndNote><Cite><Author>Cheng</Author><Year>2011</Year><RecNum>12</RecNum><DisplayText><styleface="superscript">[27]</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358925">12</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Cheng,MingMing</author><author>Zhang,GuoXin</author><author>Mitra,NiloyJ.</author><author>Huang,Xiaolei</author><author>Hu,ShiMin</author></authors></contributors><titles><title>Globalcontrastbasedsalientregiondetection</title></titles><pages>409-416</pages><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>[27]提出了一种基于区域对比度的显著对象提取算法。该模型使用了图像的全局信息和空间信息，计算图像的全局对比度和空间加权得分，结合两者之间的结果来得到图像的显著性区域。显著提高了显著性图像的精确度，降低了算法的复杂度，并且得到了高分辨率的显著性图片。上述方法都是基于传统的图像特征得到图像的显著性区域，其在简单环境下得到的显著性图形表现良好，但是由于缺乏图像的语义信息。在复杂环境下表现不佳。近些年来，随着卷积神经网络的兴起，深度学习（DeepLearning）网络逐渐应用到图像显著物体检测当中ADDINEN.CITEADDINEN.CITE.DATA[28-32]，它能够同时提取图像的低层细节特征，同时也能够得到图像的高层语义特征，极大的提高了显著物体监测在复杂环境下的应用。HouQADDINEN.CITE<EndNote><Cite><Author>Hou</Author><Year>2016</Year><RecNum>11</RecNum><DisplayText><styleface="superscript">[33]</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358924">11</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Hou,Qibin</author><author>Cheng,MingMing</author><author>Hu,Xiaowei</author><author>Borji,Ali</author><author>Tu,Zhuowen</author><author>Torr,PhilipH.S.</author></authors></contributors><titles><title>Deeplysupervisedsalientobjectdetectionwithshortconnections</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>815-828</pages><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>[33]提出了一种基于深度监督的短连接显著目标检测算法，其原理是在HED（Holistically-NestedEdgeDetection）之上，添加了一个跳过层结构，跳过层结构是指使用高层语义信息来指导低层的信息，从而高效、快速的检测到图像的显著性物体。Tsung-YiLinADDINEN.CITE<EndNote><Cite><Author>Lin</Author><Year>2017</Year><RecNum>13</RecNum><DisplayText><styleface="superscript">[34]</style></DisplayText><record><rec-number>13</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358926">13</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Lin,TsungYi</author><author>Dollar,Piotr</author><author>Girshick,Ross</author><author>He,Kaiming</author><author>Hariharan,Bharath</author><author>Belongie,Serge</author></authors></contributors><titles><title>FeaturePyramidNetworksforObjectDetection</title><secondary-title>2017IEEEConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[34]提出了一种特征金字塔网络（FPN），网络在侧向连接的基础上加入了自顶向下的连接，将底层特征与高层特征融合到一起，得到不同分辨率的特征图，并且他们都包含了原来顶层特征图的语义信息。随着卷积神经网络的不断发展，显著物体检测算法也得到了不断进步，除了上述介绍的几种方法之外，也涌现出了一大批优秀的算法，比如对比先验和流体金字塔的显著目标检测算法ADDINEN.CITE<EndNote><Cite><Author>Zhao</Author><Year>2020</Year><RecNum>14</RecNum><DisplayText><styleface="superscript">[35]</style></DisplayText><record><rec-number>14</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358928">14</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Zhao,JiaXing</author><author>Cao,Yang</author><author>Fan,DengPing</author><author>Cheng,MingMing</author><author>Li,XuanYi</author><author>Zhang,Le</author></authors></contributors><titles><title>ContrastPriorandFluidPyramidIntegrationforRGBDSalientObjectDetection</title><secondary-title>2019IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[35]、用于边界感知的显著目标检测的注意力反馈网络ADDINEN.CITE<EndNote><Cite><Author>Feng</Author><Year>2020</Year><RecNum>15</RecNum><DisplayText><styleface="superscript">[36]</style></DisplayText><record><rec-number>15</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358929">15</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Feng,Mengyang</author><author>Lu,Huchuan</author><author>Ding,Errui</author></authors></contributors><titles><title>AttentiveFeedbackNetworkforBoundary-AwareSalientObjectDetection</title><secondary-title>2019IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[36]、利用字幕增强显著对象语义检测ADDINEN.CITE<EndNote><Cite><Author>Zhang</Author><Year>2019</Year><RecNum>16</RecNum><DisplayText><styleface="superscript">[37]</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358930">16</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Zhang,Lu</author><author>Zhang,Jianming</author><author>Lin,Zhe</author><author>Lu,Huchuan</author><author>He,You</author></authors></contributors><titles><title>CapSal:LeveragingCaptioningtoBoostSemanticsforSalientObjectDetection</title><secondary-title>2019IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2019</year></dates><urls></urls></record></Cite></EndNote>[37]、一种具有交织多监督的显著目标检测的互学习方法ADDINEN.CITE<EndNote><Cite><Author>Wu</Author><Year>2020</Year><RecNum>17</RecNum><DisplayText><styleface="superscript">[38]</style></DisplayText><record><rec-number>17</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1615358931">17</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Wu,Runmin</author><author>Feng,Mengyang</author><author>Guan,Wenlong</author><author>Wang,Dong</author><author>Lu,Huchuan</author><author>Ding,Errui</author></authors></contributors><titles><title>AMutualLearningMethodforSalientObjectDetectionWithIntertwinedMulti-Supervision</title><secondary-title>2019IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[38]等等，其结果都非常优秀，但是他们都是将原始的RGB图像作为模型的输入，没有很好的利用图像的深度信息。伴随着科技的发展，特别是例如AzureKinectDK相机的出现，想要获取RGBD图像也越来越便捷，如图1-2所示，当RGB图像无法区分前景与背景区域时，加入深度信息ADDINEN.CITEADDINEN.CITE.DATA[39,40]则可以明显区分。因此，基于RGBD的显著物体检测算法ADDINEN.CITEADDINEN.CITE.DATA[41-45]也成为主流，例如ChenADDINEN.CITE<EndNote><Cite><Author>Chen</Author><Year>2020</Year><RecNum>54</RecNum><DisplayText><styleface="superscript">[46]</style></DisplayText><record><rec-number>54</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1618519209">54</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Chen,Shuhan</author><author>Fu,Yun</author></authors></contributors><titles><title>ProgressivelyguidedalternaterefinementnetworkforRGB-Dsalientobjectdetection</title><secondary-title>EuropeanConferenceonComputerVision</secondary-title></titles><pages>520-538</pages><dates><year>2020</year></dates><publisher>Springer</publisher><urls></urls></record></Cite></EndNote>[46]提出了一种逐步引导的替代细化网络来对其进行细化，首先通过从头开始学习来构建轻量级的深度流，而不是使用ImageNet预训练的骨干网，它可以更有效地提取互补特征，并且减少冗余。然后，与现有的基于融合的方法不同，将RGB和深度特征交替输入到建议的引导残差（GR）块中，以减少它们的相互退化。通过在每个侧面输出中的堆叠GR块中分配渐进式引导，可以很好地纠正错误检测和丢失的细节信息。KerenFuADDINEN.CITE<EndNote><Cite><Author>Fu</Author><Year>2020</Year><RecNum>49</RecNum><DisplayText><styleface="superscript">[47]</style></DisplayText><record><rec-number>49</rec-number><foreign-keys><keyapp="EN"db-id="szz9z0dz209ftkefdv1pep0h0sz99drfdaat"timestamp="1618505829">49</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Fu,Keren</author><author>Fan,Deng-Ping</author><author>Ji,Ge-Peng</author><author>Zhao,Qijun</author></authors></contributors><titles><title>Jl-dcf:Jointlearninganddensely-cooperativefusionframeworkforrgb-dsalientobjectdetection</title><secondary-title>ProceedingsoftheIEEE/CVFconferenceoncomputervisionandpatternrecognition</secondary-title></titles><pages>3052-3062</pages><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[47]提出了一种新颖的RGBD联合学习和密集协作融合（JL-DCF）架构的显著物体检测模型。JL-DCF模型通过连体网络从RGB和深度输入中学习。为此，文章提出了两个有效的组成部分：联合学习（JL）和密度合作融合（DCF）。JL模块提供了强大的显著性特征学习，而后者则是为补充性特征发现而引入的。图1-2深度图像示意图Fig.1-2Schematicdiagramofdepthimage参考文献[1]RenS,HeK,GirshickR,etal.FasterR-CNN:TowardsReal-TimeObjectDetectionwithRegionProposalNetworks[J].IEEETransactionsonPatternAnalysisandMachineIntelligence,2017,39(6):1137-1149.[2]ZhaoZ,ZhengP,XuS,etal.ObjectDetectionWithDeepLearning:AReview[J].IEEETransactionsonNeuralNetworksandLearningSystems,2019,30(11):3212-3232.[3]LinZ,ZhangZ,ChenLZ,etal.InteractiveImageSegmentationWithFirstClickAttention[C].2020IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR),2020:13336-13345.[4]ZhangS,LiewJH,WeiY,etal.InteractiveObjectSegmentationWithInside-OutsideGuidance[C].2020IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR),2020:12231-12241.[5]WengX,WangY,ManY,etal.GNN3DMOT:GraphNeuralNetworkfor3DMulti-ObjectTrackingWith2D-3DMulti-FeatureLearning[C].2020IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR),2020:6498-6507.[6]YinJ,WangW,MengQ,etal.AUnifiedObjectMotionandAffinityModelforOnlineMulti-ObjectTracking[C].2020IEEE/CVFConferenceonComputerVisionandPatternRecognition(CVPR),2