Enhancing the laws filter descriptor on DTCWT coefficients by thresholding approach for texture classification. (2018) - Dash, Sonali; Jena, Uma Ranjan
In this paper, we propose a new approach of combining dual-tree complex wavelet transform with traditional Laws' filter descriptor for texture classification using thresholding method. The dual-tree complex wavelet transform (DTCWT) is a recent technique to discrete wavelet transform, with important additional properties. It has been observed that the thresholding is a method to keep significant information of the image while discarding the unimportant part. On this basis for further enhancement of texture classification, we have acquired the texture images by applying thresholding technique to the entire texture database. These thresholded images are then applied to the fusion model of DTCWT with Laws' filter descriptor. We verify the effectiveness of the proposed method by utilising two texture databases such as Brodatz and UIUC. The proposed methods are compared with the classical Laws' filter descriptor. Results demonstrate that the proposed method greatly enhanced the classification accuracies by using k-NN as classifier.