Open AccessPerformance Analysis of Distance Metric for Content Based Image Retrieval
Author(s) -
David Sandborg Cyril O-',
E. R. Vimina
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8610.088619
Subject(s) - image retrieval , content based image retrieval , feature (linguistics) , computer science , pattern recognition (psychology) , metric (unit) , feature vector , feature detection (computer vision) , visual word , image (mathematics) , artificial intelligence , automatic image annotation , set (abstract data type) , matching (statistics) , feature extraction , information retrieval , image processing , mathematics , linguistics , philosophy , operations management , statistics , economics , programming language
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.