AGLV: An Effective Texture Classification Method Based on Local Binary Pattern (original) (raw)
Local binary pattern (LBP) is local texture descriptor widely used in texture classification due to its computational simplicity. However, the LBP descriptor has some weakness such as very sensitive to image rotation, image noise and illumination. In addition to this, LBP consider only sign difference and ignores magnitude difference that reduces its discrimination ability. In order to overcome these weaknesses of original LBP, this research work proposed new texture classification method based on LBP called Average Gray Level Value (AGLV). To make AGLV method rotation invariant and illumination invariant image is divided into 3*3 regions and use average gray level value of each region to calculate Sign Difference, Magnitude Difference, Region Based Gray Level value, Min-Max value and β value features from image. All these features capture more detail texture information for rotation invariant and illumination invariant texture classification. The AGLV method use KNN and SVM classifier for texture classification. The performance of proposed AGLV method is tested using Brodatz, Kylberg and Kth-Tips database. The experiment result shows that, the proposed AGLV method is rotation invariant and illumination invariant. It achieves higher classification result as compare to original LBP method. The texture classification result achieved by AGLV method by using kylberg texture database is 98.00%, brodatz database 92.02% and using Kth-Tips database is 42.50%.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.