Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

Abstract

One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.