A Robust Image Retrieval Method Using Multi-Hierarchical Agglomerative Clustering and Davis-Bouldin Index

Abstract

An image retrieval system is required to provide high accuracy in a short time. Combining various features will usually increase accuracy but also increase retrieval time. This study developed a CBIR (Content-Based Image Retrieval) method based on hierarchical clustering on low-level features. Low-level features consisting of color, texture, and shape are extracted and then clustered hierarchically. The resulting clusters are then validated to obtain their optimal number. In the retrieval process, the query image features are extracted and compared with the cluster centroid on each feature. The scores of query results on each feature are normalized, and then the normalized scores are weighted to get the total score. The experiment was carried out using three datasets, namely DIKE20, Corel-1k, and Corel-10k. Based on the experimental result, the proposed method shows better performance compared to the existing state-of-the-art method. On the Corel-1k and Corel-10k datasets, the proposed method obtained precision scores of 0.81 and 0.62, respectively