Ordonez, V., Deng, J., Choi, Y., Berg, A.C. and Berg, T.L. (2013) From Large Scale Image Categorization to Entry-Level Categories. Proceedings of the IEEE International Conference on Computer Vision.

has been cited by the following article:

• TITLE: Survey on Clustering Techniques for Image Categorization Dataset

  AUTHORS: 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

  KEYWORDS: Categorization, CBIR, Classifications, Clustering, Dataset

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: 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.