Pattern Recognition, International Conference on
Download PDF

Abstract

Support vector machines (SVMs) with autocorrelation kernels are applied to texture classification invariant to similarity transformations and noise. The inner product of autocorrelation functions of an arbitrary order is effectively calculated through the 2nd-order crosscorrelation of original data. Texture classification experiments show that higher performance of SVMs is achieved by exploiting the autocorrelation kernels.
Like what you’re reading?
Already a member?
Get this article FREE with a new membership!

Related Articles