2-3 September 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120206
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.
Abstract: In image retrieval with relevant feedback, classification and distance calculation have a great influence on image retrieval accuracy. In this paper, we propose an image retrieval method, called ODLDA (Image Retrieval using the optimal distance and linear discriminant analysis). The proposed method can effectively exploit user’s feedback from relevant and irrelevant image sets, which uses linear discriminant analysis to find a linear projection with an improved similarity measure. The experimental results performed on the two benchmark datasets have confirmed the superiority of the proposed method.
Keywords: Content-based image retrieval; deep learning; similarity measures; Mahalanobis metric distance; linear discriminant analysis
Phuong Nguyen Thi Lan, Tao Ngo Quoc, Quynh Dao Thi Thuy and Minh-Huong Ngo, “Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120206
@article{Lan2021,
title = {Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120206},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120206},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Phuong Nguyen Thi Lan and Tao Ngo Quoc and Quynh Dao Thi Thuy and Minh-Huong Ngo}
}
