Paper Info
Title
A Visual Saliency-Based Approach For Content-Based Image Retrieval
Abstract
During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the "semantic gap," which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.
Year
DOI
Venue
2021
10.4018/IJCINI.2021010101
INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE
Keywords
DocType
Volume
Image Retrieval, Query-Based Image Retrieval, Region of Interest
Journal
15
Issue
ISSN
Citations 
1
1557-3958
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Aamir Khan100.34
Anand Singh Jalal213828.45