Machine-Learning-Based Image Feature Selection

View Sample PDF

Author(s): Vivek K. Verma (Manipal University Jaipur, India)and Tarun Jain (Manipal University Jaipur, India)
Copyright: 2022
Pages: 9
Source title: Research Anthology on Machine Learning Techniques, Methods, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6291-1.ch048

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View Machine-Learning-Based Image Feature Selection on the publisher's website for pricing and purchasing information.

Abstract

This is the age of big data where aggregating information is simple and keeping it economical. Tragically, as the measure of machine intelligible data builds, the capacity to comprehend and make utilization of it doesn't keep pace with its development. In content-based image retrieval (CBIR) applications, every database needs its comparing parameter setting for feature extraction. CBIR is the application of computer vision techniques to the image retrieval problem that is the problem of searching for digital images in large databases. In any case, the vast majority of the CBIR frameworks perform ordering by an arrangement of settled and pre-particular parameters. All the major machine-learning-based search algorithms have discussed in this chapter for better understanding related with the image retrieval accuracy. The efficiency of FS using machine learning compared with some other search algorithms and observed for the improvement of the CBIR system.

The IRMA Community

Research IRM

Machine-Learning-Based Image Feature Selection

Purchase

Abstract

Related Content

IRMA Sponsors