Computer Science ›› 2017, Vol. 44 ›› Issue (5): 308-313.doi: 10.11896/j.issn.1002-137X.2017.05.057

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Multi-object Segmentation of Image Scene Based on Object Recognition and Saliency Detection

LI Qing, YUAN Jia-zheng and LIU Hong-zhe   

  • Online:2018-11-13 Published:2018-11-13

Abstract: This paper proposed a multi-object segmentation method of image scene based on object recognition and salien-cy detection.The object detector is learned on the training set,and then is used to locate the object in the test image with visualization of its bounding box.The test image is over-segmented into a set of superpixels.According to the location of bounding box and the superpixel-level propobilities,the region of interest is fixed.Then,a saliency map is obtained through a three-scale saliency detection.In the region of interest,a CRF model is established among the neighbo-ring superpixels,whose nodes indicate the superpixels and edges indicate their neighborhood.The saliency of a superpi-xel is embedded into the weight of relative node,and the feature difference between two neighboring superpixels is embedded into the weight of relative edge.Thus,the multi-object segmentation task is transformed into a multi-labeling task.Finally,the CRF formulation is optimized using graph cut algorithm to get the multi-object segmentation result.The experimental results show the good performance of our method.

Key words: Image segmentation,Semantic labeling,Object reasoning

[1] SHOTTON J,WINN J M,ROTHER C,et al.Textonboost for image understanding:Multi-class object recognition and segmentation by jointly modeling texture,layout,and context[J].International Journal of Computer Vision,2009,1(1):2-23.
[2] XIAO J,QUAN L.Multiple view semantic segmentation forstreet view images [C]∥The 12th International Conference on Computer Vision.2009:686-693.
[3] YAO J,FIDLER S,URTASUN R.Describing the scene as a whole:Joint object detection,scene classification and semantic segmentation [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:702-709.
[4] REN X,BO L,FOX D.RGB-(D) scene labeling:Features andalgorithms [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:2759-2766.
[5] LIU C,YUEN J,TORRALBA A.Nonparametric scene parsing:Label transfer via dense scene alignment [C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:1972-1979.
[6] ZHANG H,XIAO J,QUAN L.Supervised label transfer for semantic segmentation of street scenes [C]∥Proceedings of European conference on Computer Vision.2010:561-574.
[7] TIGHE J,LAZEBNIK S.Superparsing:Scalable nonparametric image parsing with superpixels [C]∥Proceedings of European Conference on Computer Vision.2010:352-365.
[8] CHEN X,LI Q,SONG Y,et al.Supervised geodesic propagation for semantic label transfer[C]∥Proceedings of European conference on Computer Vision.2012:553-565.
[9] ZHANG H,FANG T,CHEN X,et al.Partial similarity basednonparametric scene parsing in certain environment [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2011:2241-2248.
[10] PRICE B L,MORSE B S,COHEN S.Geodesic graph cut for interactive image segmentation [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2010:3161-3168.
[11] WU J,ZHAO Y,ZHU J,et al.Milcut:A sweeping line multiple instance learning paradigm for interactive image segmentation [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2014:256-263.
[12] ROTHER C,MINKA T P,BLAKE A,et al.Cosegmentation of image pairs by histogram matching-incorporating a global constraint into mrfs [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2006:993-1000.
[13] VICENTE S,KOLMOGOROV V,ROTHER C.Cosegmenta-tion revisited:Models and optimization [C]∥11th European Conference on Computer Vision.2010:465-479.
[14] VICENTE S,ROTHER C,KOLMOGOROV V.Object coseg-mentation [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2011:2217-2224.
[15] BATRA D,KOWDLE A,PARIKH D,et al.Interactively co-segmentating topically related images with intelligent scribble guidance [J].International Journal of Computer Vision,2011,93(3):273-292.
[16] BAI X,WANG J,SAPIRO G.Dynamic Color Flow:A Motion-Adaptive Color Model for Object Segmentation in Video[C]∥Proceedings of European Conference on Computer Vision.2010:617-630.
[17] BAI X,WANG J,SAPIRO G.Towards temporally-coherent video matting[C]∥International Conference on Computer Vision/Graphics Collaboration Techniques and Applications.Mirage 2011:63-74.
[18] HE X,GOULD S.An exemplar-based CRF for multi-instanceobject segmentation [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2014:296-303.
[19] TIGHE J,LAZEBNIK S.Finding things:Image parsing with regions and per-exemplar detectors [C]∥IEEE Conference on Computer Vision and Pattern Recognition.2013:3001-3008.
[20] MALISIEWICZ T,GUPTA A,EFROS A A.Ensemble of exemplar-SVMs for object detection and beyond[C]∥IEEE International Conference on Computer Vision.2011:89-96.
[21] ARBELAEZ P,MAIRE M,FOWLKES C,et al.Contour Detection and Hierarchical Image Segmentation[J] IEEE TPAMI,2011,3(5):898-916.
[22] GOFERMAN S,ZELNIK-MANOR L,TAL A.Context-Aware Saliency Detection[J].IEEE Trans.Pattern Anal.Mach.Intell.,2012,4(10):1915-1926.
[23] CHENG M,MITRA N J,HUANG X,et al.Global Contrast Based Salient Region Detection[J].IEEE Trans.Pattern Anal.Mach.Intell.,2015,7(3):569-582.
[24] BOYKOV Y,VEKSLER O,ZABIH R.Efficient ApproximateEnergy Minimization via Graph Cuts[J].IEEE TPAMI,2001,20(12):1222-1239.
[25] BOYKOV Y,KOLMOGOROV V.An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision[J].IEEE TPAMI,2004,26(9):1124-1137.

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