Survey on Clustering Techniques for Image Categorization Dataset  

作　　者：Mohd Afizi Mohd Shukran Mohd Sidek Fadhil Mohd Yunus Muhammad Naim Abdullah Mohd Rizal Mohd Isa Mohammad Adib Khairuddin Kamaruzaman Maskat Suhaila Ismail Abdul Samad Shibghatullah Mohd Afizi Mohd Shukran;Mohd Sidek Fadhil Mohd Yunus;Muhammad Naim Abdullah;Mohd Rizal Mohd Isa;Mohammad Adib Khairuddin;Kamaruzaman Maskat;Suhaila Ismail;Abdul Samad Shibghatullah(Universiti Pertahanan Nasional Malaysia, Kuala Lumpur, Malaysia;University Malaysia of Computer Science & Engineering (UNIMY), Cyberjaya, Malaysia;UCSI University, Kuala Lumpur, Malaysia)

机构地区：[1]Universiti Pertahanan Nasional Malaysia, Kuala Lumpur, Malaysia [2]University Malaysia of Computer Science & Engineering (UNIMY), Cyberjaya, Malaysia [3]UCSI University, Kuala Lumpur, Malaysia

出　　处：《Journal of Computer and Communications》2022年第6期177-185,共9页电脑和通信（英文）

摘　　要：Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massive image collection. For an image, user’s definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper;CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massive image collection. For an image, user’s definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper;CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.

关 键 词：CATEGORIZATION CBIR CLASSIFICATIONS CLUSTERING DATASET 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]