SGN: Sequential Grouping Networks for Instance Segmentation

Abstract

In this paper, we propose Sequential Grouping Networks (SGN) to tackle the problem of object instance segmentation. SGNs employ a sequence of neural networks, each solving a sub-grouping problem of increasing semantic complexity in order to gradually compose objects out of pixels. In particular, the first network aims to group pixels along each image row and column by predicting horizontal and vertical object breakpoints. These breakpoints are then used to create line segments. By exploiting two-directional information, the second network groups horizontal and vertical lines into connected components. Finally, the third network groups the connected components into object instances. Our experiments show that our SGN significantly outperforms state-of-the-art approaches in both, the Cityscapes dataset as well as PASCAL VOC. © 2017 IEEE.

Keywords

Pascal (unit)Computer sciencePixelSegmentationObject (grammar)Image segmentationArtificial intelligencePattern recognition (psychology)Artificial neural networkLine segmentSequence (biology)Line (geometry)Computer visionMathematics

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Publication Info

Year: 2017
Type: article
Citations: 295
Access: Closed

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APA Style

                            
                                    Shu Liu, 
                                
                                    Jiaya Jia, 
                                
                                    Sanja Fidler
                                
                                et al.
                            
                            (2017). 
                            SGN: Sequential Grouping Networks for Instance Segmentation. 
                            
                            .
                            https://doi.org/10.1109/iccv.2017.378

Identifiers

DOI: 10.1109/iccv.2017.378