Publication

Abstract : Local binary descriptors are extensively used for image representation in many of the computer vision applications. A majority of these local binary descriptors exploit the intensity difference of the neighbouring pixels with respect to the centre pixel of the chosen region to formulate the representative value at the respective pixel position. In this paper, a novel descriptor, called Local Triangular Coded Pattern (LTCP), is introduced that utilises the relationship between a set of pixels in the triangular neighbourhood of a region to compute the descriptor. Unlike many of the other local binary descriptors, the proposed descriptor considers multiple pixels as centres within the given region to obtain the binary pattern. The performance of the LTCP descriptor is analysed by performing image classification in benchmarked texture datasets such as KTH-TIPS, Outex, Brodatz and Kylergb and in facial emotion datasets such as CK+, JAFFE, MUFE and Yale Face. The results indicate that LTCP with the Random Forest classifier gives an accuracy of 92.82%, 93.81%, 94.11% and 97.14%, respectively, on Brodatz, Outex, KTH-TIPS and Kylergb datasets for texture classification and 97.52%, 95.52%, 96.13% and 93.88%, respectively, on CK+, JAFFE, MUFE and Yale Face datasets for emotion classification. The experimental findings reflect the LTCP descriptor’s dominance and robustness over others.