This paper is published in Volume-2, Issue-6, 2016
Area
Pattern Recognition
Author
Dr. R. Malini
Org/Univ
Anna University, Tamil Nadu, India
Paper ID
V2I6-1183
Publisher
Keywords
Euclidean Distance, Harris Detector, Principal Component Analysis, SIFT, Feature Descriptor

Citationsacebook

IEEE
Dr. R. Malini. An Enhanced Image Descriptor Algorithm for Image Retrieval using SIFT - PCA, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Dr. R. Malini (2016). An Enhanced Image Descriptor Algorithm for Image Retrieval using SIFT - PCA. International Journal of Advance Research, Ideas and Innovations in Technology, 2(6) www.IJARIIT.com.

MLA
Dr. R. Malini. "An Enhanced Image Descriptor Algorithm for Image Retrieval using SIFT - PCA." International Journal of Advance Research, Ideas and Innovations in Technology 2.6 (2016). www.IJARIIT.com.

Abstract

Due to the growth of internet and social media sites there has been a proliferation of images which makes image retrieval a challenging job. The proposed an Enhanced Image Descriptor Algorithm for Image Retrieval using SIFT-PCA involves three steps: feature detection (Harris Detector), feature description (SIFT), dimension reduction (PCA). The features of all images in database are retrieved and stored in feature database in the form of histogram. When a query image is given, the generated histogram of query image is matched with feature database of all image histograms and those images with minimum distance are retrieved. Euclidean distance is used as the distance measure. For analyzing the performance of the algorithm, average precision and recall are used. Precision is the measure of ability of a system to present all the relevant items and Recall is defined as the ability to present only relevant items.