An Efficient CNN-Based Method for Content-Based Image Retrieval

Author:

A Mukul Kumar Patro 1,Dr. J Bhuvana 1

Affiliation:

1. School of Computer Science and IT, Jain University, Bangalore, Karnataka, India

Abstract

Image recovery has been one of the most fascinating and active study fields in the field of computer vision. The use of content-based image retrieval (CBIR) systems allows for the automatic indexing, searching, retrieval, and exploration of picture datasets. Important characteristics in content-based picture retrieval systems include colour - texture elements. As a result, content-based image retrieval (CBIR) is attractive as a source of precise and speedy retrieval in the modern era. The (CBIR) system uses a feature-based approach to retrieve images from image databases. Low grade characteristics and high grade characteristics are the two categories that image features fall under. Low level aspects of an image include colour, texture, and shape, whereas high level features define the image's semantic content. CBIR is a rapidly developing technology, and as datasets grow as a result of recent advancements in multimedia, it is crucial to enhance this technology to suit user needs.

Publisher

Technoscience Academy

Subject

General Medicine

Reference11 articles.

1. Schaefer, Gerald. "An introduction to content-based image retrieval." Eighth International Conference on Digital Information Management (ICDIM 2013).

2. Dharani, T., and I. Laurence Aroquiaraj. "A survey on content based image retrieval." Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on. IEEE, 2013.

3. Kaur, Sukhman, and Rupinder Kaur. “Survey of Content Based Image Retrieval Architecture, Advantages and Disadvantages.” International Journal of Research in Electronics and Computer Engineering, vol. 4, no. 3, July 2016, pp. 108–110, https://doi.org/10.13140/RG.2.2.22093.03043.

4. Sirisha Kopparthi, Dr. N. K. Kameswara Rao, “Content based Image Retrieval using Deep Learning Technique with Distance Measures”, Science, Technology & Human Values 9(12):251-261, 2020.

5. S. Mangijao Singh, K. Hemachandran, “Image Retrieval based on the Combination of Color Histogram and Color Moment”, International Journal of Computer Applications, Volume 58– No.3, November 2012.

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