Performance Analysis of Distance Metric for Content Based Image Retrieval

Abstract

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.This paper implemented different distance measures over the wang's dataset and compared the results obtained for content based image retrieval using four features. The features used are, maLBP, mdLBP, LBP and RGB. The results infer that the cityblock distance measure gives the best results for CBIR. City block distance measure got an average improvement of 12.575%, 21.202, 36.035% and 15.083% improvement when the maLBP, mdLBP, LBP and RGB features respectively are used for retrieval.Experience: 17 years of teaching and research experience.