Cart (Loading....) | Create Account
Close category search window
 

Multiscale Anomaly Detection Using Diffusion Maps

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Mishne, G. ; Dept. of Electr. Eng., Technion - Israel Inst. of Technol., Haifa, Israel ; Cohen, I.

We propose a multiscale approach to anomaly detection in images, combining spectral dimensionality reduction and a nearest-neighbor-based anomaly score. We use diffusion maps to embed the data in a low dimensional representation, which separates the anomaly from the background. The diffusion distance between points is then used to estimate the local density of each pixel in the new embedding. The diffusion map is constructed based on a subset of samples from the image and then extended to all other pixels. Due to the interpolative nature of extension methods, this may limit the ability of the diffusion map to reveal the presence of the anomaly in the data. To overcome this limitation, we propose a multiscale approach based on Gaussian pyramid representation, which drives the sampling process to ensure separability of the anomaly from the background clutter. The algorithm is successfully tested on side-scan sonar images of sea-mines.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:7 ,  Issue: 1 )

Date of Publication:

Feb. 2013

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.