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Information Journal Paper

Title

A Multilayered Complex Network Model for Image Retrieval

Pages

  36-42

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 approach.

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    APA: Copy

    Shakibian, Hadi, & MOGHADAM CHARKARI, NASROLLAH. (2021). A Multilayered Complex Network Model for Image Retrieval. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 13(4), 36-42. SID. https://sid.ir/paper/969853/en

    Vancouver: Copy

    Shakibian Hadi, MOGHADAM CHARKARI NASROLLAH. A Multilayered Complex Network Model for Image Retrieval. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2021;13(4):36-42. Available from: https://sid.ir/paper/969853/en

    IEEE: Copy

    Hadi Shakibian, and NASROLLAH MOGHADAM CHARKARI, “A Multilayered Complex Network Model for Image Retrieval,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 13, no. 4, pp. 36–42, 2021, [Online]. Available: https://sid.ir/paper/969853/en

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