Share Email Print

Proceedings Paper

Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The rapid growth of different types of images has posed a great challenge to the scientific fraternity. As the images are increasing everyday, it is becoming a challenging task to organize the images for efficient and easy access. The field of image retrieval attempts to solve this problem through various techniques. This paper proposes a novel technique of image retrieval by combining Scale Invariant Feature Transform (SIFT) and Co-occurrence matrix. For construction of feature vector, SIFT descriptors of gray scale images are computed and normalized using z-score normalization followed by construction of Gray-Level Co-occurrence Matrix (GLCM) of normalized SIFT keypoints. The constructed feature vector is matched with those of images in database to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and the performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods.

Paper Details

Date Published: 19 June 2017
PDF: 6 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430V (19 June 2017);
Show Author Affiliations
Prashant Srivastava, Univ. of Allahabad (India)
Manish Khare, Gwangju Institute of Science and Technology (Korea, Republic of)
Ashish Khare, Univ. of Allahabad (India)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?