With the rapid development of computer, computer vision derived from computer vision has also made important progress in the field of image research. The extraction of image information is the most basic work in the field of image research. However, in the current environment, there is still a lack of effective methods to understand more complex image problems, such as image shape, material and illumination distribution in the environment. Eigenimage decomposition can be achieved by obtaining albedo eigenvalues and luminance eigenvalues. The color and illumination information of the image can be obtained more intuitively. Based on this, this paper proposes an intrinsic image decomposition method based on depth learning and probability graph model, in order to extract image information more accurately. Firstly, a deep convolution neural network is trained to decompose reflectivity image and shadow image. Then the conditional random field is used to optimize the reflectivity image and shadow image. The convolutional neural network designed in this paper obtains preliminary results through multi-scale architecture, deep supervision, step-by-step refinement of synthetic images and multi-stage training, which has been significantly improved compared with previous algorithms. Then the essential image and the corresponding gradient image are further optimized by conditional random field, and the eigenvalue image with richer details and clearer boundary can be obtained.
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Bell S, Bala K, Suavely N (2014) Intrinsic images in the wild. ACM Trans Graph (TOG) 33(4):159
Bian Z, Tang P, Yan J (2019) Land-cover classification from multiple classifiers using decision fusion based on the probabilistic graphical model. Int J Remote Sens 40(12):1–17
Dong J (2015) Research on fast and reliable template image matching technology. National University of Defense Science and Technology
Du J (2017) Research on pixel-level multiscale medical image fusion method. Chongqing University of Posts and Telecommunications
Ferrari D, Niks D, Yang LH et al (2003) Allosteric communication in the tryptophan synthase bienzyme complex: roles of the β-subunit aspartate 305−Arginine 141 Salt Bridge. Biochemistry 42(25):7807–7818
Hao Y, Mingxin Y, Jiabin X, Lianqing Z, Tao Z, Zhihui Z (2019) Tongue squamous cell carcinoma discrimination with Raman spectroscopy and convolutional neural networks. Vib Spectrosc 103:102938
Kim S, Park K, Sohn K et al (2016) Unified depth prediction and intrinsicdecomposition from a single image via joint convolutional neural fields. European Conference on Computer Vision. Springer International Publishing, pp 143–159
Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques—adaptive computation and machine learning. MIT Press, London
Li Y, Brown MS (2014) Single image layer separation using relative smoothness. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2752–2759
Liu L, Zhang J, Fu X et al (2019) Unsupervised segmentation and elm for fabric defect image classification. Multimedia Tools Appl 78(9):12421–12449
Narihira T, Maire M, Yu SX (2015) Direct intrinsic Learning albedo-shadingdecomposition by convolutional regression. Proceedings of the IEEE International Conference on Computer Vision, pp 2992–2992
Ren Z, Wu L (2018) Hyperspectral intrinsic image decomposition based on automatic subspace partitioning. Adv Laser Optoelectron 55(10):398–404
Roberto R-R, Edgar G, Ke P, Dang KN, Frédéric L, Philippe P, Lima-Saad WE (2019) Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals. Comput Biol Med 111:103355. https://doi.org/10.1016/j.compbiomed.2019.103355
Sarishvili A, Winter J, Luhmann HJ, Mildenberger E (2019) Probabilistic graphical model identifies clusters of EEG patterns in recordings from neonates. Clin Neurophysiol 130(8):1342–1350
Science—Computational Science; Reports on Computational Science Findings from University of Milan Provide New Insights (Efficient Computational Strategies To Learn the Structure of Probabilistic Graphical Models of Cumulative Phenomena)
Scipioni M, Giorgetti A, Latta DD et al (2018) Direct parametric maps estimation from dynamic PET data: an iterated conditional modes approach. J Healthc Eng 4:1–14
Shen Z, Yuan S (2019) Regional load clustering ensemble forecasting using convolutional neural network support vector regression machine. Power Grid Technol 10:15. https://doi.org/10.13335/j.1000-3673.pst.2019.0759
Sun J, Yan H (2018) Research on expression classification method based on probability graph model. J Liaoning Univ Eng Technol (Natural Science Edition) 37(06):932–938
Sun L, Xie J, Wang C (2019) Collection development for macao studies—a user perspective. Collect Manage 44(2):1–15
Wang H (2016) Image-based visualization of plant leaf aging process. Shenyang Agricultural University
Wang L, Zhong Y, Li Z, He Y (2019) On-line fabric defect detection algorithm based on in-depth learning. Comput Appl 1–6 [2019-04-01]
Xu J (2016) Research on probabilistic diagnosis method of multi-fault program. Dalian Maritime University
Xu J, Zhang D, Qian W (2017) Application of probabilistic graph model in social network user similarity discovery. Comput Sci Explor 11(07):1056–1067
Yang B (2015) Geometric feature extraction and shape restoration algorithm based on RGB-D image. Zhengzhou University
Yang J (2016) Image fusion algorithm based on two-dimensional empirical mode decomposition. Northwest University of Technology
Zhu R, Wei H, Lu Y, Sun D (2015) Research on license plate enhancement algorithm under uneven illumination. Minicomput Syst 36(03):601–604
This work was supported by the National Key Research Development Program of China [grant number 2017YFB0802800].
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Li, X., Hu, G. & Pan, Z. An eigendecomposition method based on deep learning and probabilistic graph model. J Ambient Intell Human Comput 11, 3627–3637 (2020). https://doi.org/10.1007/s12652-019-01555-0
- Eigendecomposition method
- Deep learning
- Probability graph model
- Convolutional neural network
- Conditional random field