An Abstraction Model for Semantic Segmentation Algorithms

Semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, sematic segmentation is used in many tasks such as cancer detection, robot-assisted surgery, satellite image analysis, self-driving car control, etc. In this process, accuracy and efficiency are the two crucial goals for this purpose, and there are several state of the art neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and reducing the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researches to achieve a comprehensive view of the field. To offer a comprehensive view, in this paper, an abstraction model for the task of semantic segmentation is offered. The proposed framework consists of four general blocks that cover the majority of majority of methods that have been proposed for semantic segmentation. We also compare different approaches and consider the importance of each part in the overall performance of a method.

[1]  Shadrokh Samavi,et al.  A new fast approach to nonparametric scene parsing , 2014, Pattern Recognit. Lett..

[2]  Shadrokh Samavi,et al.  Image retargeting using nonparametric semantic segmentation , 2014, Multimedia Tools and Applications.

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

[4]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gregory Shakhnarovich,et al.  Feedforward semantic segmentation with zoom-out features , 2014, CVPR.

[6]  Pengfei Xiong,et al.  Pyramid Attention Network for Semantic Segmentation , 2018, BMVC.

[7]  Ian D. Reid,et al.  RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Uwe Stilla,et al.  Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection , 2016, ISPRS Journal of Photogrammetry and Remote Sensing.

[9]  Jianbo Shi,et al.  Semantic Segmentation with Boundary Neural Fields , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Marios Anthimopoulos,et al.  Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks , 2018, IEEE Journal of Biomedical and Health Informatics.

[11]  Hayit Greenspan,et al.  Fully Convolutional Network for Liver Segmentation and Lesions Detection , 2016, LABELS/DLMIA@MICCAI.

[12]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Martin Thoma,et al.  A Survey of Semantic Segmentation , 2016, ArXiv.

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

[15]  A. Anbarasa Pandian,et al.  A Survey: Analysis on Pre-processing and Segmentation Techniques for Medical Images , 2016 .

[16]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Yassine Ruichek,et al.  Survey on semantic segmentation using deep learning techniques , 2019, Neurocomputing.

[18]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[19]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[20]  Wei Li,et al.  DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[22]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

[24]  Dwi H. Widyantoro,et al.  Traffic lights detection and recognition based on color segmentation and circle hough transform , 2015, 2015 International Conference on Data and Software Engineering (ICoDSE).

[25]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[26]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[27]  Alexander Rakhlin,et al.  Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning , 2018, bioRxiv.

[28]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Yang Zhang,et al.  Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT , 2017, 2017 Chinese Automation Congress (CAC).

[30]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Peter Reinartz,et al.  Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Cyrill Stachniss,et al.  Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[34]  Yoshua Bengio,et al.  ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[35]  Keisuke Nemoto,et al.  Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[37]  MALLU MALLU,et al.  Fashion Object Detection and Pixel-Wise Semantic Segmentation : Crowdsourcing framework for image bounding box detection a Pixel-Wise Segmentation , 2018 .

[38]  Roberto Cipolla,et al.  Convolutional CRFs for Semantic Segmentation , 2018, BMVC.

[39]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[40]  Massimo Mauro,et al.  FASSEG: A FAce semantic SEGmentation repository for face image analysis , 2019, Data in brief.

[41]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Xiao Xiang Zhu,et al.  Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[43]  Yang Wang,et al.  Future Semantic Segmentation with Convolutional LSTM , 2018, BMVC.

[44]  Guo Lei,et al.  Weak supervised image semantic segmentation method based on superpixels and conditional random field , 2018 .