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Research Article | Open Access |

Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis

Author 1: Phuong Nguyen Thi Lan Author 2: Tao Ngo Quoc Author 3: Quynh Dao Thi Thuy Author 4: Minh-Huong Ngo
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 2 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.0120206

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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

How to Cite this Article

Lan, P. N. T., Quoc, T. N., Thuy, Q. D. T., & Ngo, M. (2021). Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis. International Journal of Advanced Computer Science and Applications, 12(2). https://doi.org/10.14569/IJACSA.2021.0120206

Lan, Phuong Nguyen Thi, et al.. "Improve the Effectiveness of Image Retrieval by Combining the Optimal Distance and Linear Discriminant Analysis." International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, 2021, https://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},
  volume    = {12},
  number    = {2},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Phuong Nguyen Thi Lan and Tao Ngo Quoc and Quynh Dao Thi Thuy and Minh-Huong Ngo},
  doi       = {10.14569/IJACSA.2021.0120206},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120206}
}

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