ICIAP 1995: Image Analysis and Processing pp 393-398 | Cite as
Robust features for textures in additive noise
Abstract
The paper describes a method for texture classification in noise by using third-order cumulants as discriminating features. The problem is formulated as a test on K hypotheses and solved by a Maximum Likelihood (ML) criterium applied in the third-order cumulant domain. Since in the case of image processing complete third-order cumulant computation is not feasible, we reduced the estimation to a limited number of cumulant slices and lags. This reduction makes the classification algorithm suboptimal. Thus, a criterion for the choice of cumulant samples to be computed is introduced in order to guarantee the selection of those lags which better identify the different textures in the training phase of the classifier.
Experimental tests are carried out to evaluate third-order cumulant performances on noisy textures and the importance of lags selection.
Keywords
Gaussian Noise Correct Classification Texture Classification High Order Statistic Classification ProbabilityReferences
- [1]C.L. Nikias and J.M. Mendel: Signal processing with higher-order spectra. IEEE Signal Processing Magazine, pp. 10–37 (1993).Google Scholar
- [2]G.B. Giannakis and M.K. Tsatsanis: A unifying maximum-likelihood view of cumulant and polyspectral measures for non-Gaussian signal classification and estimation. IEEE Trans. on Information Theory, 38, pp. 386–406 (1992).Google Scholar
- [3]A. Makovec, and G. Ramponi: Supervised Discrimination of noisy textures using third-order moments. Fourth COST WG.1 and 2nd Workshop on Adaptive Methods and Emerging Techniques for Signal Processing and Communications, Slovenia, April 1994.Google Scholar
- [4]D.R. Brillinger and M. Rosenblatt: Asymptotic theory of estimates of kth-order spectra. In: Spectral Analysis of Time Series, B.Harris, ed., Wiley, NY, pp. 153–188, (1967).Google Scholar