COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS

Authors

  • Zahraa H. Al-Obaide alobaide
  • Ayad A. Al-Ani

DOI:

https://doi.org/10.31987/ijict.5.1.182

Keywords:

Image Retrieval, CBIR, Curvelet Transform, Feature Extraction, Fast Fourier transform, Gabor filter, wavelet transformation, Texture Feature, Euclidean distance.

Abstract

Searching for a relevant image in an archive is a problematic research issue for the computer vision research community. The majority of search engines retrieve images using traditional text-based approaches that rely on captions and metadata. Extensive research has been reported in the last two decades for content-based image retrieval (CBIR), analysis, and image classification. Content-Based Image Retrieval is a process that provides a framework for image search, and low-level visual features are commonly used to retrieve the images from the image database. The essential requirement in any image retrieval process is to sort the images with a close similarity in terms of visual appearance. The shape, color, and texture are examples of low-level image features. In image classification-based models and CBIR, high-level image visuals are expressed in the form of feature vectors made up of numerical values. The researcher's findings a significant gap between human visual comprehension and image feature representation. In this paper, we plan to present a comparison study and a comprehensive overview of the recent developments in the field of CBIR and image representation.

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Published

2022-04-29

How to Cite

Al-Obaide, Z. H. ., & Al-Ani, A. A. . (2022). COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS. Iraqi Journal of Information and Communication Technology, 5(1), 16–30. https://doi.org/10.31987/ijict.5.1.182