Parallel implementation of local derivative pattern algorithm for fast image retrieval

The advancements in the field of internet and cloud computing has resulted in a huge amount of multimedia data and processing of this data have become more complex and computationally intensive. As a result, it has become very challenging for image retrieval algorithms to efficiently extract useful information from these data. Local Derivative Pattern (LDP) is a higher order local pattern operator used for image retrieval. Originally proposed for face recognition, LDP encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. However LDP results in a very large feature vector thereby becoming computationally very expensive. In this paper, we propose efficient techniques for extracting parallelism from LDP algorithm and propose strategies for implementing it on GPGPUs. We show that with the optimal configuration of GPGPU kernels we can perform image retrieval at a much faster rate that would facilitate improved performance for image retrieval applications. We show that by porting LDP algorithm on CUDA a speedup of the order of 36X can be achieved as compared to its sequential counterpart for images having resolution of 192×192, 256×256 and 512×512 pixels.