By Topic

Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis

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.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Matthew Mellor ; REACT Eng. Ltd., Whitehaven ; Byung-Woo Hong ; Michael Brady

Textures within real images vary in brightness, contrast, scale, and skew as imaging conditions change. To enable recognition of textures in real images, it is necessary to employ a similarity measure that is invariant to these properties. Furthermore, since textures often appear on undulating surfaces, such invariances must necessarily be local rather than global. Despite these requirements, it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported. Typically, they comprise detecting feature points followed by geometric normalization prior to description. We describe a method based on invariant combinations of linear filters. Unlike previous methods, we introduce a novel family of filters, which provides scale invariance, resulting in a texture description invariant to local changes in orientation, contrast, and scale and robust to local skew. Significantly, the family of filters enables local scale invariants to be defined without using a scale selection principle or a large number of filters. A texture discrimination method based on the chi2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the Brodatz textures and the University of Illinois, Urbana-Champaign (UIUC) database, which has been designed to require local invariance.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:30 ,  Issue: 1 )