Abstract
Pseudo-labeling is an effective semi-supervised segmentation method. Most pseudo-labeling works were based on a common assumption: lower entropy means lower uncertainty. Hence many high entropy pseudo-labels are discarded and not involved in the training. Inadequate labeled data limit performance of model segmentation. In order to expand the labeled data capacity, we propose a new semi-supervised segmentation method, namely, different treatments of pixels in unlabeled images (DTP). Our DTP consists of three main components: labeled images segmentation, certain pixels segmentation, uncertain pixels segmentation. A sonar image with two different parallel segmentation networks will produce two one-hot segmentation maps. If the predictions of a pixel in the sonar image are consistent on two one-hot segmentation maps, this pixel is regarded as reliable in this segmentation and delineated as the certain pixel. On the contrary, if the prediction results are different, the pixel is delineated as the uncertain pixel. Then uncertain pixels are necessary to choose an advanced semi-supervised framework for label assignment to minimize the possible error propagation. Meanwhile, certain pixels are assessed in two extreme ways—radical segmentation and conservative segmentation. Compared with other methods, our method is novel in (1) indicating certain/uncertain pixels to expand the labeled data capacity (2) introducing other advanced semi-supervised methods for segmenting uncertain pixels to improve the segmentation performance. Experimental results show that our method achieves advanced semi-supervised segmentation performance in sonar dataset.
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17 January 2024
A Correction to this paper has been published: https://doi.org/10.1007/s13042-023-02065-4
References
Chuang MC, Hwang JN, Ye JH, Huang SC, Williams K (2017) Underwater fish tracking for moving cameras based on deformable multiple kernels. IEEE Trans Syst Man Cybern Syst 47(9):2467–2477
Karoui I, Quidu I, Legris M (2015) Automatic sea-surface obstacle detection and tracking in forward-looking sonar image sequences. IEEE Trans Geosci Remote Sens 53(11):6315–6315
Renga A et al (2014) SAR bathymetry in the Tyrrhenian Sea by COSMO-SkyMed data: a novel approach. IEEE J Sel Top Appl Earth Obs Remote Sens 7(7):2834–2847
Kostylev VE, Courtney RC, Robert G, Todd BJ (2003) Stock evaluation of giant scallop (Placopecten magellanicus) using high-resolution acoustics for seabed mapping. Fish Res 60(2–3):479–492
Brice G (1996) Salvage and the underwater cultural heritage. Mar Policy 20(4):337–342
Boulinguez D, Quinquis A (2002) 3-D underwater object recognition. IEEE J Ocean Eng 27(4):814–829
Kong WZ et al (2020) YOLOv3-DPFIN: a dual-path feature fusion neural network for robust real-time sonar target detection. IEEE Sens J 20(7):3745–3756
Bhosale YH, Patnaik KS (2022) Application of deep learning techniques in diagnosis of Covid-19 (Coronavirus): a systematic review. Neural Process Lett
Bhosale YH, Patnaik KS (2023) PulDi-COVID: chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates,". Biomed Signal Process Control 81:104445
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv preprint
Szegedy C et al (2015) Going deeper with convolutions. In: Presented at the 2015 IEEE conference on computer vision and pattern recognition (CVPR)
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Presented at the medical image computing and computer-assisted intervention, PT III
He KM, Zhang XY, Ren SQ, Sun J, IEEE (2016) Deep residual learning for image recognition. In: Presented at the 2016 IEEE conference on computer vision and pattern recognition (CVPR)
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Sarker MMK et al (2018) SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In: Presented at the medical image computing and computer assisted intervention—MICCAI 2018, PT II
Milletari F, Navab N, Ahmadi SA, IEEE (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Presented at the proceedings of 2016 fourth international conference on 3D vision (3DV)
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203
Banan A, Nasiri A, Taheri-Garavand A (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquac Eng 89:102053
Wang WC, Du YJ, Chau KW, Xu DM, Liu CJ, Ma Q (2021) An ensemble hybrid forecasting model for annual runoff based on sample entropy, secondary decomposition, and long short-term memory neural network. Water Resour Manag 35(14):4695–4726
Fan YJ, Xu KK, Wu H, Zheng Y, Tao B (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE Access 8:25111–25121
Chen WB, Sharifrazi D, Liang GX, Band SS, Chau KW, Mosavi A (2022) Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit. Eng Appl Comput Fluid Mech 16(1):965–976
Afan HA et al (2021) Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques. Eng Appl Comput Fluid Mech 15(1):1420–1439
Chen CC et al (2022) Forecast of rainfall distribution based on fixed sliding window long short-term memory. Eng Appl Comput Fluid Mech 16(1):248–261
Zhu PP, Isaacs J, Fu B, Ferrari S, IEEE (2017) Deep learning feature extraction for target recognition and classification in underwater sonar images. In: Presented at the 2017 IEEE 56th annual conference on decision and control (CDC)
Valdenegro-Toro M, IEEE (2017) Best practices in convolutional networks for forward-looking sonar image recognition. In: Presented at the OCEANS 2017—Aberdeen
Kim J, Cho H, Pyo J, Kim B, Yu SC, IEEE (2016) The convolution neural network based agent vehicle detection using forward-looking sonar image. In: Presented at the oceans 2016 MTS/IEEE Monterey
Song Y, He B, Liu P (2021) Real-time object detection for AUVs using self-cascaded convolutional neural networks. IEEE J Ocean Eng 46(1):56–67
Dzieciuch I, Gebhardt D, Barngrover C, Parikh K (2017) Non-linear convolutional neural network for automatic detection of mine-like objects in sonar imagery. In: Presented at the proceedings of the 4th international conference on applications in nonlinear dynamics (ICAND 2016)
Song Y et al (2017) Side scan sonar segmentation using deep convolutional neural network. In: Presented at the Oceans 2017—Anchorage
Wang Z, Guo JX, Huang WZ, Zhang SW (2022) Side-scan sonar image segmentation based on multi-channel fusion convolution neural networks. IEEE Sens J 22(6):5911–5928
Cao WP, Gao JZ, Ming Z, Cai SB, Shan ZG (2018) Fuzziness-based online sequential extreme learning machine for classification problems. Soft Comput 22(11):3487–3494
Cao WP et al (2021) An ensemble fuzziness-based online sequential learning approach and its application. In: Presented at the knowledge science, engineering and management, PT I
Cao WP, Yang PF, Ming Z, Cai SB, Zhang JY, IEEE (2020) An improved fuzziness based random vector functional link network for liver disease detection. Presented at the 2020 IEEE 6th int conference on big data security on cloud (bigdatasecurity)/6th IEEE int conference on high performance and smart computing, (HPSC)/5th IEEE int conference on intelligent data and security (IDS)
Laine S, Aila T (2017) Temporal ensembling for semi-supervised learning. arXiv preprint
Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Presented at the advances in neural information processing systems 30 (NIPS 2017)
Verma V et al (2022) Interpolation consistency training for semi-supervised learning. Neural Netw 145:90–106
Li X, Yu L, Chen H, Fu C-W, Xing L, Heng P-A (2020) Transformation consistent self-ensembling model for semi-supervised medical image segmentation. arXiv preprint
Yu LQ, Wang SJ, Li XM, Fu CW, Heng PA (2019) Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Presented at the medical image computing and computer assisted intervention—Miccai 2019, PT II
Shi YH et al (2022) Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. IEEE Trans Med Imaging 41(3):608–620
Zhang Y, Yang L, Chen J, Fredericksen M, Hughes DP, Chen DZ (2017) Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Presented at the international conference on medical image computing and computer-assisted intervention
Wu YC, Xu MF, Ge ZY, Cai JF, Zhang L (2021) Semi-supervised left atrium segmentation with mutual consistency training. In: Presented at the medical image computing and computer assisted intervention—Miccai 2021, PT II
Sajjadi M, Javanmardi M, Tasdizen T, IEEE (2016) Mutual exclusivity loss for semi-supervised deep learning. In: Presented at the 2016 IEEE international conference on image processing (ICIP)
Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV, Soc IC (2019) AutoAugment: learning augmentation strategies from data. In: Presented at the 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR 2019)
Cubuk ED, Zoph B, Shlens J, Le QV, Ieee Comp SOC (2020) Randaugment: Practical automated data augmentation with a reduced search space. In: Presented at the 2020 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW 2020)
Yun S et al (2019) CutMix: regularization strategy to train strong classifiers with localizable features. In: Presented at the 2019 IEEE/CVF international conference on computer vision (ICCV 2019)
Berthelot D, Carlini N, Goodfellow I, Oliver A, Papernot N, Raffel C (2019) MixMatch: a holistic approach to semi-supervised learning. In: Presented at the advances in neural information processing systems 32 (NIPS 2019)
Zheng H et al (2019) Semi-supervised segmentation of liver using adversarial learning with deep atlas prior. In: Presented at the medical image computing and computer assisted intervention—MICCAI 2019, PT VI
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Xu, H., Tong, P. & Li, Y. Different treatments of pixels in unlabeled images for semi- supervised sonar image segmentation. Int. J. Mach. Learn. & Cyber. 15, 637–646 (2024). https://doi.org/10.1007/s13042-023-01930-6
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DOI: https://doi.org/10.1007/s13042-023-01930-6