Improving the discrimination between foreground and background for semantic segmentation

One challenging problem in semantic segmentation is due to the erroneous predictions between categorical foreground and cluttered background. To address it, we propose to utilize a fused loss function to train a fully convolutional network, which aims to enhance the discrimination between foreground and background in images. In addition, we propose a pixel objectness (POS) to measure the importance of pixels. POS is able to recover some missing foreground pixels from the background. Experimental results on the PASCAL VOC 2012 dataset demonstrate our approach can achieve considerable improvements compared with the baseline counterpart, while maintaining the ease of training deep networks.

[1]  Jian Sun,et al.  Convolutional feature masking for joint object and stuff segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Alan F. Smeaton,et al.  Object Segmentation in Images using EEG Signals , 2014, ACM Multimedia.

[3]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[4]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[7]  Jing Liu,et al.  Semi- and Weakly- Supervised Semantic Segmentation with Deep Convolutional Neural Networks , 2015, ACM Multimedia.

[8]  한보형,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015 .

[9]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[10]  Vittorio Ferrari,et al.  Region-Based Semantic Segmentation with End-to-End Training , 2016, ECCV.

[11]  Guosheng Lin,et al.  Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Subhransu Maji,et al.  Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.

[13]  Guo-Jun Qi,et al.  Hierarchically Gated Deep Networks for Semantic Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Vladlen Koltun,et al.  Geodesic Object Proposals , 2014, ECCV.

[15]  Xiaoxiao Li,et al.  Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Jian Sun,et al.  BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Mohammad Rastegari,et al.  CNN-aware binary MAP for general semantic segmentation , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[18]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  George Papandreou,et al.  Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Trevor Darrell,et al.  Constrained Convolutional Neural Networks for Weakly Supervised Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Jitendra Malik,et al.  Simultaneous Detection and Segmentation , 2014, ECCV.

[23]  Fei-Fei Li,et al.  What's the Point: Semantic Segmentation with Point Supervision , 2015, ECCV.

[24]  Lei Guo,et al.  Semantic Segmentation based on Stacked Discriminative Autoencoders and Context-Constrained Weakly Supervised Learning , 2015, ACM Multimedia.

[25]  Jitendra Malik,et al.  Semantic segmentation using regions and parts , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Yan Wang,et al.  DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[28]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[29]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[30]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.