Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing
Nowadays the great interest of researchers in the problem of processing the interrelated data arrays including images is retained. In the modern theory of machine learning, the problem of image processing is often viewed as a problem in the field of graph models. Image pixels constitute a unique arr...
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| Format: | Article |
| Language: | English |
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Computer Vision Center Press
2014-06-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.es/article/view/626 |
| _version_ | 1831559079383793664 |
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| author | Dinh Sang |
| author_facet | Dinh Sang |
| author_sort | Dinh Sang |
| collection | DOAJ |
| description | Nowadays the great interest of researchers in the problem of processing the interrelated data arrays including images is retained. In the modern theory of machine learning, the problem of image processing is often viewed as a problem in the field of graph models. Image pixels constitute a unique array of interrelated elements. The interrelations between array elements are represented by an adjacency graph. The problem of image processing is often solved by minimizing Gibbs energy associated with corresponding adjacency graphs. The crucial disadvantage of Gibbs approach is that it requires empirical specifying of appropriate energy functions on cliques. In the present work, we investigate a simpler, but not less effective model, which is an expansion of the Markov chain theory. Our approach to image processing is based on the idea of replacing the arbitrary adjacency graphs by tree-like (acyclic in general) ones and linearly combining of acyclic Markov models in order to get the best quality of restoration of hidden classes. In this work, we propose algorithms for tuning combination of acyclic adjacency graphs. |
| first_indexed | 2024-12-17T05:20:36Z |
| format | Article |
| id | doaj.art-79f9235293cd427b9bc0d2509e871e4d |
| institution | Directory Open Access Journal |
| issn | 1577-5097 |
| language | English |
| last_indexed | 2024-12-17T05:20:36Z |
| publishDate | 2014-06-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj.art-79f9235293cd427b9bc0d2509e871e4d2022-12-21T22:01:58ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972014-06-0113210.5565/rev/elcvia.626239Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processingDinh Sang0Hanoi University of Science and Technology, Viet NamNowadays the great interest of researchers in the problem of processing the interrelated data arrays including images is retained. In the modern theory of machine learning, the problem of image processing is often viewed as a problem in the field of graph models. Image pixels constitute a unique array of interrelated elements. The interrelations between array elements are represented by an adjacency graph. The problem of image processing is often solved by minimizing Gibbs energy associated with corresponding adjacency graphs. The crucial disadvantage of Gibbs approach is that it requires empirical specifying of appropriate energy functions on cliques. In the present work, we investigate a simpler, but not less effective model, which is an expansion of the Markov chain theory. Our approach to image processing is based on the idea of replacing the arbitrary adjacency graphs by tree-like (acyclic in general) ones and linearly combining of acyclic Markov models in order to get the best quality of restoration of hidden classes. In this work, we propose algorithms for tuning combination of acyclic adjacency graphs.https://elcvia.cvc.uab.es/article/view/626Image ProcessingImage SegmentationSupervised LearningHidden Markov ModelMarkov ChainGraph Model |
| spellingShingle | Dinh Sang Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing ELCVIA Electronic Letters on Computer Vision and Image Analysis Image Processing Image Segmentation Supervised Learning Hidden Markov Model Markov Chain Graph Model |
| title | Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| title_full | Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| title_fullStr | Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| title_full_unstemmed | Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| title_short | Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| title_sort | algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing |
| topic | Image Processing Image Segmentation Supervised Learning Hidden Markov Model Markov Chain Graph Model |
| url | https://elcvia.cvc.uab.es/article/view/626 |
| work_keys_str_mv | AT dinhsang algorithmsforselectingparametersofcombinationofacyclicadjacencygraphsintheproblemoftextureimageprocessing |