A Study of Distance Metrics in Histogram Based Image Retrieval

Abhijeet Kumar Sinha; K.K. Shukla

doi:10.24297/ijct.v4i3.4205

Authors

Abhijeet Kumar Sinha Indian Institute of Technology (BHU), Varanasi
K.K. Shukla IIT(BHU), Varanasi

DOI:

https://doi.org/10.24297/ijct.v4i3.4205

Keywords:

Content-based Image Retrieval (CBIR), Euclidean distance, Manhattan distance, Vector Cosine Angle distance, Histogram Intersection Distance, COREL database

Abstract

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.

In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.