Comparative Evaluation of Classical Methods, Optimized Gabor Filters and LBP for Texture Feature Selection and Classification

This paper builds upon a previous texture feature selection and classification methodology by extending it with two state-of-the-art families of texture feature extraction methods, namely Manjunath & Ma's Gabor wavelet filters and Local Binary Pattern operators (LBP), which are integrated with more classical families of texture filters, such as co-occurrence matrices, Laws filters and wavelet transforms. Results with Brodatz compositions and outdoor images are evaluated and discussed, being the basis for a comparative study about the discrimination capabilities of those different families of texture methods, which have been traditionally applied on their own.