Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers

Abstract

In this paper we present a novel wrinkle evaluation method that uses modified wavelet coefficients and optimized support-vector-machine (SVM) classifications to characterize and classify the wrinkling appearance of fabric. Fabric images were decomposed with the wavelet transform, and five parameters were defined, based on the modified wavelet coefficients, to describe wrinkling features, such as orientation, hardness, density, and contrast. These parameters were also used as the inputs of optimized SVM classifiers to obtain overall wrinkle grading in accordance with the standard American Association of Textile Chemists and Colorists smoothness appearance (SA) replicas. The SVM classifiers, based on a linear kernel and a radial-basis-function kernel, were used in the study. The effectiveness of this evaluation method was tested by 300 images of five selected fabrics that had different fiber contents, weave structures, colors, and laundering cycles. The cross-validation tests on the SA classifications indicated that the SA grades of more than 75% of these diversified samples could be recognized correctly. The extracted wrinkle parameters provided useful information for textile, appliance, and detergent manufactures to inspect wrinkling behaviors of fabrics.