A Multilayered Complex Network Model for Image Retrieval

Hadi i Shakibian, Nasrollah Moghadam Charkari
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Abstract

2021 Abstract —In this study, an image retrieval system is proposed based on complex network model. Assuming a prior image categorization, firstly, a multilayered complex network is constructed between the images of each category according to the color, texture, and shape features. Secondly, by defining a meta-path as the way of connecting two images in the network, a set of informative meta-paths are composed to find the similar images by exploring the network. The established complex network provides an efficient way to benefit from the image correlations to enhance the similarity search of the images. On the other hand, employing diverse meta-paths with different semantics leads to measuring the image similarities based on effective image features for each category. The primary results indicate the efficiency and validity of the proposed
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一种多层复杂网络图像检索模型
摘要在本研究中,提出了一种基于复杂网络模型的图像检索系统。在先验图像分类的前提下,首先根据图像的颜色、纹理和形状特征,在每一类图像之间构建多层复杂网络;其次,通过将元路径定义为连接网络中两张图像的方式,组成一组信息元路径,通过对网络的探索来寻找相似的图像。建立的复杂网络提供了一种有效的方法来利用图像的相关性来增强图像的相似性搜索。另一方面,使用不同语义的元路径导致基于每个类别的有效图像特征来测量图像相似度。初步结果表明了该方法的有效性和有效性
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