Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach

Authors

  • Van-Hieu Vu Information Technology Faculty, Haiphong University, Haiphong
  • Truong-Thang Nguyen Institute of Information Technology, Vietnam Academy of Science and Technology, 456Ho Chi Minh
  • Huu-Quynh Nguyen Electric Power University in Vietnam, Hanoi
  • Quoc-Tao Ngo Institute of Information Technology, Vietnam Academy of Science and Technology, 456Ho Chi Minh

DOI:

https://doi.org/10.26713/jims.v10i1-2.456

Keywords:

Content based image retrieval, Relevance feedback, Classification, Pareto point

Abstract

Machine learning methods have demonstrated promising performance for Content Based Image Retrieval (CBIR) using Relevance Feedback (RF). However, a very limited number of feedback images can significantly degrade the performance of these techniques. In this work, each image is represented by a vector of multiple distance measures corresponding to multiple features. Each feature is considered a sub-query for RF process. In RF process, we propose to use Pareto method to get Pareto points (also called trade-off points) according to different depths. These points are used as relevant queries for the next RF round. Experimental results show that our proposed approach is very effective to improve the performance of the classification engine.

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Published

2018-08-10
CITATION

How to Cite

Vu, V.-H., Nguyen, T.-T., Nguyen, H.-Q., & Ngo, Q.-T. (2018). Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach. Journal of Informatics and Mathematical Sciences, 10(1-2), 93–108. https://doi.org/10.26713/jims.v10i1-2.456

Issue

Vol. 10 No. 1-2 (2018)

Section

Research Articles

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