Machine learning methods, one type of methods used in artificial
intelligence, are now widely used to analyze two-dimensional (2D) images in
various fields. In these analyses, estimating the boundary between two regions
is basic but important. If the model contains stochastic factors such as random
observation errors, determining the boundary is not easy. When the probability
distributions are mis-specified, ordinal methods such as probit and logit
maximum likelihood estimators (MLE) have large biases. The grouping estimator
is a semiparametric estimator based on the grouping of data that does not
require specific probability distributions. For 2D images, the grouping is
simple. Monte Carlo experiments show that the grouping estimator clearly
improves the probit MLE in many cases. The grouping estimator essentially makes
the resolution density lower, and the present findings imply that methods using
low-resolution image analyses might not be the proper ones in high-density
image analyses. It is necessary to combine and compare the results of high- and
low-resolution image analyses. The grouping estimator may provide theoretical
justifications for such analysis.
References
[1]
De la Calleja, J. and Fuentes, O. (2004) Machine Learning and Image Analysis for Morphological Galaxy Classification. Monthly Notices of the Royal Astronomical Society, 349, 87-93. https://doi.org/10.1111/j.1365-2966.2004.07442.x
[2]
Duro, D.C., Franklin, S.E. and Dubé, M.G. (2012) A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery. Remote Sensing of Environment, 118, 259-272. https://doi.org/10.1016/j.rse.2011.11.020
[3]
Li, X., Cheng, X., Chen, W., et al. (2015) Identification of Forested Landslides Using LiDar Data, Object-Based Image Analysis, and Machine Learning Algorithms. Remote Sensing, 7, 9705-9726. https://doi.org/10.3390/rs70809705
[4]
Arganda-Carreras, I., Kaynig, V., Rueden, C., et al. (2017) Trainable Weka Segmentation: A Machine Learning Tool for Microscopy Pixel Classification. Bioinformatics, 33, 2424-2426. https://doi.org/10.1093/bioinformatics/btx180
[5]
Kan, A. (2017) Machine Learning Applications in Cell Image Analysis. Immunology and Cell Biology, 95, 525-530. https://doi.org/10.1038/icb.2017.16
[6]
Zhang, Y.C. and Kagen, A.C. (2017) Machine Learning Interface for Medical Image Analysis. Journal of Digital Imaging, 30, 615-621.
https://doi.org/10.1007/s10278-016-9910-0
[7]
Bulat, A. and Tzimiropoulos, G. (2018) Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs. 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, 18-23 June 2018, 109-117.
https://doi.org/10.1109/CVPR.2018.00019
[8]
Komura, D. and Ishikawa, S. (2019) Machine Learning Approaches for Pathologic Diagnosis. Virchows Archiv, 475, 131-138.
https://doi.org/10.1007/s00428-019-02594-w
Bi, Q., Goodman, K.E., Kaminsky, J., et al. (2020) What Is Machine Learning? A Primer for the Epidemiologist. American Journal of Epidemiology, 188, 2222-2239.
https://doi.org/10.1093/aje/kwz189
[11]
Kose, K., Bozkurt, A., Alessi-Fox, C., et al. (2020) Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy. Journal of Investigative Dermatology, 140, 1214-1222. https://doi.org/10.1016/j.jid.2019.10.018
[12]
Tang, H. and Hu, Z. (2020) Research on Medical Image Classification Based on Machine Learning. IEEE Access, 8, 93145-93154.
https://doi.org/10.1109/ACCESS.2020.2993887
[13]
Turner, O.C., Aeffner, F. and Bangari, D.S. (2020) Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicologic Pathology, 48, 277-294.
https://doi.org/10.1177/0192623319881401
[14]
Wei, P.W., He, F. and Zou, Y. (2020) Content Semantic Image Analysis and Storage Method Based on Intelligent Computing of Machine Learning Annotation. Neural Computing and Applications, 32, 1813-1822.
https://doi.org/10.1007/s00521-020-04739-4
[15]
Li, J., Shao, S. and Hong, J. (2021) Machine Learning Shadowgraph for Particle Size and Shape Characterization. Measurement Science and Technology, 32, Article ID: 015406. https://doi.org/10.1088/1361-6501/abae90
[16]
Santhi, K. and Reddy, A.R.M. (2021) An Automated Framework for Coronary Analysis from Coronary Cine Angiograms Using Machine Learning and Image Analysis Techniques. IT in Industry, 9, 1406-1412.
https://doi.org/10.17762/itii.v9i1.284
[17]
Sato, S., Maki, S., Yamanaka, T., et al. (2021) Machine Learning-Based Image Analysis for Accelerating the Diagnosis of Complicated Preneoplastic and Neoplastic Ductal Lesions in Breast Biopsy Tissues. Breast Cancer Research and Treatment, 188, 649-659. https://doi.org/10.1007/s10549-021-06243-2
[18]
Botero, U.J., Lson, R., Lu, H., et al. (2021) Hardware Trust and Assurance through Reverse Engineering: A Tutorial and Outlook from Image Analysis and Machine Learning Perspectives. ACM Journal on Emerging Technologies in Computing Systems, 17, Article 62. https://doi.org/10.1145/3464959
[19]
Tang, X., Kusmartseva, I., Kulkarni, S., et al. (2021) Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas. American Journal of Pathology, 191, 454-462.
https://doi.org/10.1016/j.ajpath.2020.11.010
[20]
Wang, P., Fan, E. and Wang, P. (2021) Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning. Pattern Recognition Letters, 141, 61-67. https://doi.org/10.1016/j.patrec.2020.07.042
[21]
Liakos, K.G., Busato, P., Moshou, D., et al. (2018) Machine Learning in Agriculture: A Review. Sensors, 18, 2674. https://doi.org/10.3390/s18082674
[22]
Gewali, U.B., Monteiro, S.T. and Saber, E. (2019) Machine Learning Based Hyperspectral Image Analysis: A Survey.
[23]
Martin-Isla, C., Campello, V.M., Izquierdo, C., et al. (2020) Image-Based Cardiac Diagnosis with Machine Learning: A Review. Frontiers in Cardiovascular Medicine, 7, Article No. 1. https://doi.org/10.3389/fcvm.2020.00001
[24]
Zahia, S., Zapirain, M.B.G. and Sevillano, X. (2020) Pressure Injury Image Analysis with Machine Learning Techniques: A systematic Review on Previous and Possible Future Methods. Artificial Intelligence in Medicine, 102, Article ID: 101742.
https://doi.org/10.1016/j.artmed.2019.101742
[25]
de Matos, J., Ataky, S.T.M., de Souza Britto Jr., A., et al. (2021) Machine Learning Methods for Histopathological Image Analysis: A Review. Electronics, 10, 562.
https://doi.org/10.3390/electronics10050562
[26]
Brown, S. (2021) Machine Learning, Explained. MIT Sloan School of Management, Cambridge.
https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
[27]
Mahesh, B. (2020) Machine Learning Algorithms—A Review. International Journal of Science and Research, 9, 381-386.
[28]
Ma, X., Niu, Y., Gu, L., Wang, Y., et al. (2021) Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems. American Journal of Pathology, 110, Article ID: 107332. https://doi.org/10.1016/j.patcog.2020.107332
[29]
Nawata, K. (1994) Estimation of the Boundary of the Two Regions by the Grouping Method. Journal of the Japan Statistical Society, 24, 14-35.
[30]
Nawata, K. (1990) Robust Estimation Based on Grouped-Adjusted Data in Linear Regression Models. Journal of Econometrics, 43, 317-336.
https://doi.org/10.1016/0304-4076(90)90123-B
[31]
Nawata, K. (1990) Robust Estimation Based on Grouped-Adjusted Data in Censored Regression Models. Journal of Econometrics, 43, 337-362.
https://doi.org/10.1016/0304-4076(90)90124-C
[32]
Amemiya, T. (1985) Advanced Econometrics. Harvard University Press, Cambridge.
[33]
Miguel-Hurtado, O., Guest, R., Stevenage, S.V., et al. (2016) Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. PLoS ONE, 11, e0165521.
https://doi.org/10.1371/journal.pone.0165521
[34]
Bennett, G. (1962) Probability Inequalities for Sum of Independent Random Variables. Journal of the American Statistical Association, 57, 33-45.
https://doi.org/10.1080/01621459.1962.10482149
[35]
Mojica, E., Pertuz, S. and Arguello, H. (2017) High-Resolution Coded-Aperture Design for Compressive X-Ray tomography Using Low Resolution Detectors. Optic Communications, 404, 103-109. https://doi.org/10.1016/j.optcom.2017.06.053
[36]
Singh, S., Guo, Y., Winiarski, B., et al. (2018) High Resolution Low kV EBSD of Heavily Deformed and Nanocrystalline Aluminum by Dictionary-Based Indexing. Scientific Reports, 8, Article No. 10991. https://doi.org/10.1038/s41598-018-29315-8
[37]
Li, Y., Xu, L., et al. (2019) A Y-Net Deep Learning Method for Road Segmentation Using High-Resolution Visible Remote Sensing Images. Remote Sensing Letters, 10, 381-390. https://doi.org/10.1080/2150704X.2018.1557791
[38]
Alganci, U., Soydas, M. and Sertel, E. (2020) Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sensing, 12, 458. https://doi.org/10.3390/rs12030458
[39]
Yi, Z., Tang, Q., Azizi, S., et al. (2020) Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 14-19 June 2020, 7508-7517. https://doi.org/10.1109/CVPR42600.2020.00753
[40]
Cao, Y. and Huang, X. (2021) A Deep Learning Method for Building Height Estimation Using High-Resolution Multi-View Imagery over Urban Areas: A Case Study of 42 Chinese Cities. Remote Sensing of Environment, 264, Article ID: 112590.
https://doi.org/10.1016/j.rse.2021.112590
[41]
Giles, A.B., Davies, J.E., Ren, K., et al. (2021) A Deep Learning Algorithm to Detect and Classify Sun Glint from High-Resolution Aerial Imagery over Shallow Marine Environments. Journal of Photogrammetry and Remote Sensing, 181, 20-26.
https://doi.org/10.1016/j.isprsjprs.2021.09.004
[42]
Horwath, J.P., Zakharov, D.N., Mégret, R., et al. (2021) Understanding Important Features of Deep Learning Models for Segmentation of High-Resolution Transmission Electron Microscopy Images. Computational Materials, 6, Article No. 108.
https://doi.org/10.1038/s41524-020-00363-x
[43]
Wen, Q., Luo, Z., Chen, R., et al. (2021) Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators. Sensors, 21, 1033.
https://doi.org/10.3390/s21041033
[44]
Zamboni, P., Marcato Junior, J., de Andrade Silva, J., et al. (2021) Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images. Remote Sensing, 13, 2482.
https://doi.org/10.3390/rs13132482
[45]
Karsa, A., Punwani, S. and Shmueli, K. (2018) The Effect of Low Resolution and Coverage on the Accuracy of Susceptibility Mapping. Magnetic Resonace in Medicine, 81, 1833-1848. https://doi.org/10.1002/mrm.27542
[46]
Yu, X., Fernando, B., Hartley, R. and Porikli, F. (2018) Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 908-917. https://doi.org/10.1109/CVPR.2018.00101
[47]
Kuhlbrodt, T., Jones, C.G., Sellar, A., et al. (2018) The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate. Journal of Advances in Modeling Earth Systems, 10, 2865-2888.
https://doi.org/10.1029/2018MS001370
[48]
Wang, S., Zhang, K., et al. (2020) Physically-Based Landslide Prediction over a Large Region: Scaling Low-Resolution Hydrological Model Results for High-Resolution Slope, Stability Assessment. Environmental Modelling and Software, 124, Article ID: 104607. https://doi.org/10.1016/j.envsoft.2019.104607
[49]
Shao, Z., Zhou, Z., Huang, X., et al. (2021) MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images. Remote Sensing, 13, 239. https://doi.org/10.3390/rs13020239
[50]
Xu, H., Li, X., Zhang, K., et al. (2021) SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting. Algorithms, 14, 236.
https://doi.org/10.3390/a14080236
[51]
Weilharter, A. and Fraudorfer, F. (2021) HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction from High-Resolution Images. IEEE Access, 9, 11306-11315. https://doi.org/10.1109/ACCESS.2021.3050556
[52]
Saito, S., Simon, T., Saragih, J., et al. (2020) PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 84-93. https://doi.org/10.1109/CVPR42600.2020.00016
