A Semantic Modeling Approach for Medical Image Semantic Retrieval Using Hybrid Bayesian Networks

Lin, Chin E.; Yin, Jingwei; Gao, Xue; Chen, Jianyu; Qin, Pei

doi:10.1109/isda.2006.253884

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…5000 images in the core image library are selected to form the background image library of the image retrieval system. 5000 Corel images are composed of 50 categories, such as Africans, beaches, buildings, buses, dinosaurs, elephants, flowers, horses, and mountains; each category has 100 images [29]. e specific process of image example retrieval using GMM-MMP method is as follows: first, the user inputs the example image to be retrieved, the system reads the image similarity semantic model parameters, calculates the posterior pseudo-probability function values of the image pairs composed of all the images to be retrieved and the example images in the image database, and arranges all the images to be retrieved in descending order according to the posterior pseudo-probability function values.…”

Section: Example Retrievalmentioning

confidence: 99%

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Guo

2022

Mathematical Problems in Engineering

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Content-based image retrieval (CBIR) is an important part of pattern recognition and artificial intelligence. It has broad application prospects in many important fields, such as digital library, medical image analysis, petroleum geological survey, and public security information retrieval. In this study, statistical modeling and discriminant learning methods are used to analyze and study some key problems in image retrieval, including image concept retrieval, image example retrieval, and relevance feedback. The main research results obtained are as follows: an image classification method based on the Gaussian mixture model (GMM) and max-min posterior pseudo-probability (MMP) discriminant learning is proposed, which is called GMM-MMP method for short; a concept retrieval method based on GMM-MMP is proposed. According to the image concept, the image is divided into two categories: the concept-related image and the concept-unrelated image. The Gaussian mixture model is used to establish the mapping from the image low-level features to the image concept, and the image is classified according to the posterior pseudo-probability classifier to realize the image concept retrieval; an example retrieval method based on GMM-MMP is proposed. According to the image similarity semantics, the image is divided into two categories: the related image and the uncorrelated image of the example image. The Gaussian mixture model is used to establish the mapping from the low-level features of the image to the image similarity semantics. The image is classified according to the posterior pseudo-probability classifier to realize the image case retrieval. Based on the above work, this study implements a content-based image retrieval system.

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Section: Example Retrievalmentioning

confidence: 99%

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Guo

2022

Mathematical Problems in Engineering

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“…Therefore, the evaluation results of several experts were integrated by using the arithmetic method in this paper. The comprehensive evaluation of n experts can be expressed as

where P i is the fuzzy probability of occurrence of i th accident, f ij is the fuzzy number of j th expert to the i th accident, and m is the amount of accidents [ 13 ].…”

Section: Fuzzy-accurate Bayesian Networkmentioning

confidence: 99%

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

Wang

Zhang

2014

The Scientific World Journal

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A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

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“…Xin Zhang et al [6] proposed an image semantic representation model (ISRM) based on statistical learning theory and smooth support vector regression (SSVR). Chun-Yi Lin et al [7] proposed a multi-level semantic modeling method, which integrates Support Vector Machines (SVM) into hybrid Bayesian networks (HBN). Monay F et al [8] learned a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images.…”

Section: Introductionmentioning

confidence: 99%

Condensed semantic tree model for image category representation

Chen

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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This paper presents a condensed semantic tree model for representing image category. For a specific application area, a semantic concept space is defined. According to the annotation for an image, a real-value semantic vector is gained that describes the content of it. In order to represent image category, condensed semantic tree model is introduced. It is a triple level structure. The bottom level is a semantic concept mask, which selects those concepts relevant to semantic category. The middle level is composed of three semantic modules, which extract high-level semantic of an image. The top level analyzes the probability that an image is belong to a specific image category. Every semantic category has different model configuration. The experimental results illustrate that the effectiveness of the proposed condensed semantic tree model is good.

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A Semantic Modeling Approach for Medical Image Semantic Retrieval Using Hybrid Bayesian Networks

Cited by 12 publications

References 10 publications

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

Condensed semantic tree model for image category representation

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