Work

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

Year: 2018

Type: article

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.

Source: Journal of Informatics and Mathematical Sciences

DOI: 10.26713/jims.v10i1-2.456

Authors: Van-Hieu Vu, Truong-Thang Nguyen, Huu-Quynh Nguyen, Quoc-Tao Ngo

Institutions: Hai Phong University, Vietnam Academy of Science and Technology, Electric Power University

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Vietnam Academy of Science and Technology

Award: G2607434046