Integration of local and global shape analysis for logo classification

Abstract

A comparison is made of global and local methods for the shape analysis of logos in an image database. The qualities of the methods are judged by using the shape signatures to define a similarity metric on the logos. As representatives for the two classes of methods, we use the negative shape method which is based on local shape information and a wavelet-based method which makes use of global information. We apply both methods to images with different kinds of degradations and examine how a particular degradation highlights the strengths and shortcomings of each method. Finally, we use these results to develop a new adaptive weighting scheme which is based on the relative performances of the two methods. This scheme gives rise to a new method that is much more robust with respect to all degradations examined and works by automatically predicting if the negative shape or wavelet method is performing better.