结合DBSCAN聚类的室内场景分割
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摘要
针对RGB-D图像具有丰富的三维几何特征,复杂度高这一具有挑战性的难题,提出一种针对室内场景RGB-D图像的分割算法.首先,经过RGB-D图像过分割生成超像素,并基于超像素之间的距离度量测量超像素之间的相似性;然后,采用DBSCAN算法将具有相似的颜色信息和几何信息的超像素聚类到一个分类中.在该聚类过程中,通过限制扩散区域来降低计算复杂度.在室内场景RGB-D图像库上大量实验结果表明,文中算法分割精确度和速率均超过了其他算法,证明了其高效性和准确性.
Aiming at the challenging problems of RGB-D images with rich 3D geometric features and high complexity, this paper proposes a segmentation algorithm for RGB-D images of indoor scenes. Firstly, generating superpixels by over-segmentation of RGB-D images and measuring the similarity of two superpixels based on the distance. Then, the DBSCAN algorithm is used to cluster the superpixels with similar color and geometric information into the same classification. In the clustering process, we restrict the diffusion area to reduce computational complexity. A lot of experimental results on the database of RGB-D images show that the segmentation accuracy and rate of our algorithm exceed the other algorithms, which proves our algorithm's efficiency and accuracy.
Aiming at the challenging problems of RGB-D images with rich 3D geometric features and high complexity, this paper proposes a segmentation algorithm for RGB-D images of indoor scenes. Firstly, generating superpixels by over-segmentation of RGB-D images and measuring the similarity of two superpixels based on the distance. Then, the DBSCAN algorithm is used to cluster the superpixels with similar color and geometric information into the same classification. In the clustering process, we restrict the diffusion area to reduce computational complexity. A lot of experimental results on the database of RGB-D images show that the segmentation accuracy and rate of our algorithm exceed the other algorithms, which proves our algorithm's efficiency and accuracy.
引文
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[31]Pan X,Zhou Y F,Li F,et al.Superpixels of RGB-D images for indoor scenes based on weighted geodesic driven metric[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(10):2342-2356
[32]Silberman N,Hoiem D,Kohli P,et al.Indoor segmentation and support inference from RGBD images[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer,2012:746-760
[33]Arbelaez P,Maire M,Fowlkes C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(5):898-916
[34]Arbelaez P.Boundary extraction in natural images using ultra-metric contour maps[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop.Los Alamitos:IEEE Computer Society Press,2006:182
[35]Levinshtein A,Stere A,Kutulakos K N,et al.TurboPixels:fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2290-2297
[36]Nowozin S,Gehler P V,Lampert C H,et al.On parameter learning in CRF-based approaches to object class image segmentation[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer,2010:98-111
[37]Hoiem D,Stehub A N,Efros A A,et al.Recovering occlusion boundaries from a single image[C]//Proceedings of the 11th IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2007:1-8
[2]Shotton J,Sharp T,Kipman A,et al.Real-time human pose recognition in parts from single depth images[J].Communications of the ACM,2013,56(1):116-124
[3]Henry P,Krainin M,Herbst E,et al.RGB-D mapping:using depth cameras for dense 3D modeling of indoor environments[J].International Journal of Robotics Research,2012,31(5):647-663
[4]Izadi S,Kim D,Hilliges O,et al.KinectFusion:real-time 3Dreconstruction and interaction using a moving depth camera[C]//Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology.New York:ACM Press,2011:559-568
[5]Gould S,Fulton R,Koller D.Decomposing a scene into geometric and semantically consistent regions[C]//Proceedings of the 12th IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2009:1-8
[6]Ladicky L,Russell C,Kohli P,et al.Associative hierarchical crfs for object class image segmentation[C]//Proceedings of the 12th IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2009:739-746
[7]Pawan Kumar M,Koller D.Efficiently selecting regions for scene understanding[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2010:3217-3224
[8]Tighe J,Lazebnik S.SuperParsing:scalable nonparametric image parsing with superpixels[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer,2010:352-365
[9]Silberman N,Fergus R.Indoor scene segmentation using a structured light sensor[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.Los Alamitos:IEEE Computer Society Press,2011:601-608
[10]Ester M,Kriegel H P,Xu X W.A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining.Palo Alto:AAAI Press,1996:226-231
[11]Chen T W,Chen Y L,Chien S Y.Fast image segmentation based on K-means clustering with histograms in HSV color space[C]//Proceedings of the 10th IEEE Workshop on Multimedia Signal Processing.Los Alamitos:IEEE Computer Society Press,2008:322-325
[12]Wu M N,Lin C C,Chang C C.Brain tumor detection using color-based K-means clustering segmentation[C]//Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing.Los Alamitos:IEEE Computer Society Press,2007:245-250
[13]Wang P,Zeng G,Gan R,et al.Structure-sensitive superpixels via geodesic distance[J].International Journal of Computer Vision,2013,103(1):1-21
[14]Saarinen K.Color image segmentation by a watershed algorithm and region adjacency graph processing[C]//Proceedings of the 1st International Conference on Image Processing.Los Alamitos:IEEE Computer Society Press,1994,3:1021-1025
[15]Beucher S,Meyer F.The morphological approach to segmentation:the watershed transformation[M]//Mathematical Morphology in Image Processing.Heidelberg:Springer,1993
[16]Meyer F,Beucher S.Morphological segmentation[J].Journal of Visual Communication and Image Representation,1990,1(1):21-46
[17]Dobrin B P,Viero T J,Gabbouj M,et al.Fast watershed algorithms:analysis and extensions[C]//Proceedings of SPIE.Bellingham:Society of Photo-Optical Instrumentation Engineers,1994,2810:209-220
[18]Boykov Y,Veksler O,Zabih R.Fast approximate energy minimization via graph cuts[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(11):1222-1239
[19]Boykov Y,Kolmogorov V.An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137
[20]Shi J B,Malik J.Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905
[21]Zhang K H,Zhang L,Song H H,et al.Active contours with selective local or global segmentation:a new formulation and level set method[J].Image and Vision Computing,2010,28(4):668-676
[22]Zhang K H,Song H H,Zhang L.Active contours driven by local image fitting energy[J].Pattern Recognition,2010,43(4):1199-1206
[23]Boykov Y,Funka-Lea G.Graph cuts and efficient N-D image segmentaion[J].International Journal of Computer Vision,2006,70(2):109-131
[24]Koppula H S,Anand A,Joachims T,et al.Semantic labeling of3D point clouds for indoor scenes[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc,2011:244-252
[25]Bo L F,Ren X F,Fox D.Kernel descriptors for visual recognition[C]//Proceedings of Advances in Neural Information Processing Systems.New York:ACM Press,2010,23:1-9
[26]Ren X F,Bo L F,Fox D.RGB-(D)scene labeling:features and algorithms[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2012:2759-2766
[27]Candès E J,Li X D,Ma Y.Robust principal component analysis?[J].Journal of the ACM,2011,58(3):Article No.11
[28]Lewis D D,Yang Y M,Rose T G,et al.A new benchmark collection for text categorization research[J].Journal of Machine Learning Research,2004,5(2):361-397
[29]Manavalan R,Thangavel K.TRUS image segmentation using morphological operators and DBSCAN clustering[C]//Proceedings of the World Congress on Information and Communication Technologies.Los Alamitos:IEEE Computer Society Press.2011:898-903
[30]Hou J,Sha C S,Chi L,et al.Merging dominant sets and DBSCAN for robust clustering and image segmentation[C]//Proceedings of the IEEE International Conference on Image Processing.Los Alamitos:IEEE Computer Society Press,2014:4422-4426
[31]Pan X,Zhou Y F,Li F,et al.Superpixels of RGB-D images for indoor scenes based on weighted geodesic driven metric[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(10):2342-2356
[32]Silberman N,Hoiem D,Kohli P,et al.Indoor segmentation and support inference from RGBD images[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer,2012:746-760
[33]Arbelaez P,Maire M,Fowlkes C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(5):898-916
[34]Arbelaez P.Boundary extraction in natural images using ultra-metric contour maps[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop.Los Alamitos:IEEE Computer Society Press,2006:182
[35]Levinshtein A,Stere A,Kutulakos K N,et al.TurboPixels:fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2290-2297
[36]Nowozin S,Gehler P V,Lampert C H,et al.On parameter learning in CRF-based approaches to object class image segmentation[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer,2010:98-111
[37]Hoiem D,Stehub A N,Efros A A,et al.Recovering occlusion boundaries from a single image[C]//Proceedings of the 11th IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2007:1-8