A New Method for Medical Image Retrieval Based on Markov Random Field

2017 ◽  
pp. 447-461
Author(s):  
Tiaodi Wang ◽  
Haiwei Pan ◽  
Xiaoqin Xie ◽  
Zhiqiang Zhang ◽  
Xiaoning Feng
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Medical Image ◽  
New Method ◽  
Medical Image Retrieval ◽  
Markov Random

scholarly journals A new method of content based medical image retrieval and its applications to CT imaging sign retrieval

2017 ◽  
Vol 66 ◽  
pp. 148-158 ◽  
Author(s):  
Ling Ma ◽  
Xiabi Liu ◽  
Yan Gao ◽  
Yanfeng Zhao ◽  
Xinming Zhao ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Ct Imaging ◽  
New Method ◽  
Medical Image Retrieval

scholarly journals Multimodal Markov Random Field for Image Reranking Based on Relevance Feedback

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ricardo Omar Chávez ◽  
Hugo Jair Escalante ◽  
Manuel Montes-y-Gómez ◽  
Luis Enrique Sucar
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Experimental Results ◽  
Feedback Information ◽  
Markov Random ◽  
Initial Retrieval ◽  
Image Reranking

This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider the problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to a query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the images in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked images. The MRF takes into account (1) the rank information provided by the initial retrieval system, (2) similarities among images in the list, and (3) relevance feedback information. Hence, the problem of image reranking is reduced to that of minimizing an energy function that represents a trade-off between image relevance and interimage similarity. The proposed MRF is a multimodal as it can take advantage of both visual and textual information by which images are described with. We report experimental results in the IAPR TC12 collection using visual and textual features to represent images. Experimental results show that our method is able to improve the ranking provided by the base retrieval system. Also, the multimodal MRF outperforms unimodal (i.e., either text-based or image-based) MRFs that we have developed in previous work. Furthermore, the proposed MRF outperforms baseline multimodal methods that combine information from unimodal MRFs.

scholarly journals A new method for direction finding based on Markov random field model

Radio Science ◽  
2015 ◽  
Vol 50 (7) ◽  
pp. 598-613 ◽  
Author(s):  
Mamoru Ota ◽  
Yoshiya Kasahara ◽  
Yoshitaka Goto
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Direction Finding ◽  
New Method ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Markov Random

scholarly journals Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model

2009 ◽  
Vol 2009 ◽  
pp. 1-17 ◽  
Author(s):  
Meng-Hsiun Tsai ◽  
Yung-Kuan Chan ◽  
Jiun-Shiang Wang ◽  
Shu-Wei Guo ◽  
Jiunn-Lin Wu
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Query Image ◽  
Retrieval Method ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random

The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations.

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