Benefiting from the growing mass of big data and the rapid development of computing efficiency, a series of advanced models of machine learning and deep learning have been proposed one after another, and have achieved great success in many application scenarios. The research purpose of this paper is to fuse the image multi-features of the restricted Boltzmann machine and the neural network in the experiment, using the convolutional restricted Boltzmann machine, to establish a model and investigate the three datasets in the ship, oil, and machine. Image fusion performance evaluation results on the test set. The results show that the model can improve the performance of image fusion to a certain extent.

Yan Wang. Image Multi-feature Fusion with Restricted Boltzmann Machine and Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 3: 28-35. https://doi.org/10.38007/NN.2022.030304.

[1] Vasamsetti S, Setia S, Mittal N, et al. Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds. Computer Vision, IET, 2018, 12(6):770-778. https://doi.org/10.1049/iet-cvi.2017.0013

[2] Moradikia M, Samadi S, Cetin M. Joint SAR Imaging and Multi-Feature Decomposition from 2-D Under-Sampled Data Via Low-Rankness Plus Sparsity Priors. Computational Imaging IEEE Transactions on, 2019, 5(1):1-16. https://doi.org/10.1109/TCI.2018.2881530

[3] Cuevas E, Becerra H, Luque A, et al. Fast multi-feature image segmentation. Applied Mathematical Modelling, 2021, 90(5):742-757. https://doi.org/10.1016/j.apm.2020.09.008

[4] Nanjun, Paoletti, Mercedes, et al. Feature Extraction with Multiscale Covariance Maps for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2):755-769. https://doi.org/10.1109/TGRS.2018.2860464

[5] Maksimovic-Moicevic S, Lukac Z, Temerinac M. Objective estimation of subjective image quality assessment using multi-parameter prediction. Image Processing, IET, 2019, 13(13):2428-2435. https://doi.org/10.1049/iet-ipr.2018.6143

[6] Fuqiang H E, Luo H, Yao X, et al. Self-adaptive Bridge bare rebar detection algorithm based on local image segmentation and multi-feature filtering. Journal of Applied Optics, 2020, 41(3):508-515. https://doi.org/10.5768/JAO202041.0302004

[7] Kapadia M R, Dr C. Content Based Medical Image Retrieval System for Accurate Disease Diagnoses Using Modified Multi Feature Fused Xception Model. Indian Journal of Computer Science and Engineering, 2021, 12(1):89-100. https://doi.org/10.21817/indjcse/2021/v12i1/211201179

[8] Xu S, Amira O, Liu J, et al. HAM-MFN: Hyperspectral and Multispectral Image Multiscale Fusion Network with RAP Loss. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7):4618-4628. https://doi.org/10.1109/TGRS.2020.2964777

[9] Kumar M, Singh P. FPR using machine learning with multifeature method. IET Image Processing, 2018, 12(10):1857-1865. https://doi.org/10.1049/iet-ipr.2017.1406

[10] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848. https://doi.org/10.1109/TPAMI.2017.2699184

[11] Fazlzadeh A, Khoshmaram A, Feyzipour A. How Quality, Value, Image, and Satisfaction Create Loyalty at an Iran Telecom. International Journal of Business & Management, 2018, 6(8):580-580.

[12] Elad M, Datsenko D. Corrigendum: Example-Based Regularization Deployed to Super-Resolution Reconstruction of a Single Image. Computer Journal, 2018, 53(7):1131-1131. https://doi.org/10.1093/comjnl/bxp097

[13] Costa P, Galdran A, Meyer M I, et al. End-to-end Adversarial Retinal Image Synthesis. IEEE Transactions on Medical Imaging, 2018, 37(99):781-791. https://doi.org/10.1109/TMI.2017.2759102

[14] Ghamisi P, Yokoya N, Li J, et al. Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art. IEEE Geoscience & Remote Sensing Magazine, 2018, 5(4):37-78. https://doi.org/10.1109/MGRS.2017.2762087

[15] Dong, Nie, Roger, et al. Medical Image Synthesis with Deep Convolutional Adversarial Networks. IEEE Transactions on Biomedical Engineering, 2018, 65(12):2720-2730. https://doi.org/10.1109/TBME.2018.2814538

[16] Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. International Journal of Computer Vision, 2020, 128(2):336-359. https://doi.org/10.1007/s11263-019-01228-7

[17] Deal M M, Nolet G. Nullspace shuttles. Geophysical Journal International, 2018, 124(2):372-380. https://doi.org/10.1111/j.1365-246X.1996.tb07027.x

[18] Alsmadi, Mutasem K. A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation. Ain Shams Engineering Journal, 2018, 9(4):697-706. https://doi.org/10.1016/j.asej.2016.03.016