Noise robust Laws’ filters based on fuzzy filters for texture classification

Laws’ mask method has achieved wide acceptance in texture analysis, however it is not robust to noise. Fuzzy filters are well known for denoising applications. This work proposes a noise-robust Laws’ mask descriptor by integrating the exiting fuzzy filters with the traditional Laws’ mask for the improvement of texture classification of noisy texture images. Images are corrupted by adding Gaussian noise of different values. These noisy images are transformed into fuzzy images through fuzzy filters of different windows. Then the texture features are extracted using Laws’ mask descriptor. To investigate the proposed techniques two texture databases i.e. Brodatz and STex are used. The proposals are assessed by comparing the performance of the traditional Laws’ mask descriptor alone and after combined with the fuzzy filters on noisy images. The k-Nearest Neighbor (k-NN) classifier is utilized in the classification task. Results indicate that the proposed approach delivers higher classification accuracy than the traditional Laws’ mask method. Hence, validate that the suggested methods significantly improve the noised texture classification. Keywords: Fuzzy filter, Laws’ mask, Texture classification, Texture feature

K-NN based automated reasoning using bilateral filter based texture descriptor for computing texture classification

Regions in the visual field can be characterized by differences in texture, brightness, colour, or other attributes. Bilateral filter is an efficient way to smooth any digital image while preserving the fine information. In bilateral filter, it has been observed that by selecting carefully, the bilateral filter range parameter and bilateral filter domain parameter the ability to smooth any arbitrary digital image while preserving the edges can be improved. This trait of bilateral filter helps to adapt it to application specific requirements. In this study, a new feature extraction method is recommended by integrating the conventional Laws’ mask method with bilateral filter, which results in the improvement of classification accuracy. The texture features are extracted by using different values of range parameter and domain parameter and are fed as input to k-Nearest Neighbor (k-NN) classifier for classification. The new fusion model is tested with Brodatz, VisTex, STex and ALOT databases. The results of the proposed method are also compared with the conventional Laws’ mask descriptor for all the aforementioned four datasets. The experimental results show that bilateral filter based Laws’ mask feature extraction technique provides better classification accuracy for all the four databases for various combinations of bilateral filter range and domain parameters. Keywords: Bilateral filter, Laws mask descriptor, Feature extraction, Texture classificatio