Rotated texture classification by improved iterative morphological decomposition

An improved algorithm based on iterative morphological decomposition (IMD) proposed by Wang et al. (1993) is described. The proposed algorithm requires less computation than the original IMD algorithm. The improved iterative morphological decomposition (IIMD) is compared with granulometric moments, multiresolution rotation-invariant SAR (MRRISAR) models and multichannel Gabor filters. It is found that the IIMD is superior to granulometric moments and MRRISAR in rotated texture classification. The IIMD may also perform better than multichannel Gabor filters by employing many different kinds of structuring elements. In the study, three kinds of pseudo rotation-invariant structuring elements, namely the disc, octagon and square, as well as a line structuring element are tested. Since the line structuring element is rotation-variant in nature, the image is rotated to different orientations of equal angular separation to find a set of primitive features. A Fourier transform is then applied to convert these features to rotation-invariant ones. An accuracy rate as high as 96% is achieved in classifying 30 classes of textured images in the experiment. It is also demonstrated that using both the normalised variance and the mean can give a better classification accuracy rate than using both the variance and the mean when classified by a simplified Bayes or Mahalanobis distance measure