International Symposium on Grids & Clouds (ISGC) 2023 in conjunction with HEPiX Spring 2023 Workshop

COMBINATION OF LOW LEVEL AND HIGH LEVEL FEATURES TO IMPROVE THE EFFICIENCY OF CONTENT BASED IMAGE RETRIEVAL BASED ON THE EMR MANIFOLD RANKING ALGORITHM

Not scheduled

20m

BHSS. Academia Sinica

Poster Presentation Track 10: Artificial Intelligence (AI)

Speaker

Dr TUYET DAO VAN (Vietnam National Space Center, Vietnam Academy of Science and Technologt)

Description

ABSTRACT
Effective Manifold Ranking (EMR) is a technique used widely in Content-based Image Retrieval (CBIR) to rank the images in a database via measuring and ranking the similarity between each image with a given query image where the images are represented by different features.
In this paper, a combined of low level and Deep features is proposed to create an EMR graph is proposed. In the details, Deep features are extracted by using pre-train CNNs which these networks have shown strong performance in the aspect of image discrimination.
Experimental results show that the combination method of low-level and high-level features significantly increased the accuracy of the EMR manifold ranking algorithm.
Keywords: CBIR, Effective Manifold Ranking, Convolution Neural Networks , Fuzzy C-means, Low level features, High level features.