In this paper, the authors propose to use Pareto approach for candidate selection. The proposed algorithm produces a compact set of candidate images when comparing with the entire dataset and this set also contains results obtained from all aggregation operator [3]. The authors also formalize main properties of Pareto front with respect to CBIR which are mainly used to propose our two algorithms. | Journal of Computer Science and Cybernetics, , (2016), 169–187 DOI no. CONTENT BASED IMAGE RETRIEVAL USING MULTIPLE FEATURES AND PARETO APPROACH VAN-HIEU VU1 , TRUONG-THANG NGUYEN2 , HUU-QUYNH NGUYEN3 , QUOC-TAO NGO2 1 Information 2 Institute Technology Faculty, Haiphong University, Haiphong, Vietnam of Information Technology, Vietnam Academy of Science and Technology 3 Information Technology Faculty, Electric Power University, Hanoi, Vietnam nqtao}@; 3 quynhnh@ 1 hieuvv@; 2 {ntthang, Abstract. There are two commonly used aggregation based approaches in Content Based Image Retrieval (CBIR) systems using multiple features (., color, shape, texture). In the first approach, the systems usually represent each image as a unified feature vector by concatenating component feature vectors and then for a query image, compute its distance measure with images in the database. Inspite of the simplicity, this approach does not emphasize the importance of each component feature. Another approach often computes the weighted linear combination of individual distance measures and the weight assignment to each is based on Relevance Feedback (RF) from a user to determine the importance of each component. In this paper, the authors propose to use Pareto approach for candidate selection. The proposed algorithm produces a compact set of candidate images when comparing with the entire dataset and this set also contains results obtained from all aggregation operator [3]. The authors also formalize main properties of Pareto front with respect to CBIR which are mainly used to propose our two algorithms. The experiments on three image collections show that our proposed approach is very effective to improve the performance of the classification engines. Keywords. Pareto point, Pareto front, content based image retrieval (CBIR), relevance feedback (RF), classification. 1. INTRODUCTION The appearance of Internet .
