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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Image retrieval based on micro-level spatial structure features and content analysis using Full Range Gaussian Markov Random Field model
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Image retrieval based on micro-level spatial structure features and content analysis using Full Range Gaussian Markov Random Field model

机译：基于微观空间结构特征的图像检索和全范围高斯马尔可夫随机场模型的内容分析

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摘要

This paper proposes a novel system, based on Full Range Gaussian Markov Random Field (FRGMRF) model with Bayesian approach for image retrieval. The color image is segmented into various regions according to its structure and nature. The segmented image is modeled to HSV color space, where V attributes to pixel values, and it ranges from 0 to 1. On HSV color space, the texture information and spatial orientation of the pixels are extracted. On each region, the model parameters, autocorrelation coefficient (ACC), and unique texture numbers are computed using the FRGMRF model. The model parameters and ACCs are formed as feature vectors (FVs) of the image. The Mahalanobis distance is applied to measure the distance between the query and target images. Moreover, associated probabilities are computed on the texture numbers on each region, and are used to compute the divergence between the query and target images using cosine function. The obtained results are compared to those of the existing systems. The comparative study reveals that the proposed system outperforms the existing systems.
机译：本文提出了一种基于全范围高斯马尔可夫随机场（FRGMRF）模型和贝叶斯方法的图像检索系统。彩色图像根据其结构和性质分为不同的区域。分割后的图像被建模为HSV色彩空间，其中V归因于像素值，范围为0到1。在HSV色彩空间上，提取了像素的纹理信息和空间方向。在每个区域上，使用FRGMRF模型计算模型参数，自相关系数（ACC）和唯一纹理编号。模型参数和ACC形成为图像的特征向量（FV）。马氏距离适用于测量查询图像与目标图像之间的距离。此外，在每个区域上的纹理编号上计算关联的概率，并使用余弦函数将其用于计算查询图像与目标图像之间的差异。将获得的结果与现有系统的结果进行比较。比较研究表明，提出的系统优于现有系统。

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