A PyTorch Operations Based Approach for Computing Local Binary Patterns

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Devrim Akgun

Abstract

Advances in machine learning frameworks like PyTorch provides users with various machine learning algorithms together with general purpose operations. PyTorch framework provides Numpy like functions and makes it practical to use computational resources for accelerating computations. Also users may define their custom layers or operations for feature extraction algorithms based on the tensor operations. In this paper, Local Binary Patterns (LBP) which is one of the important feature extraction approaches in computer vision were realized using tensor operations of PyTorch framework. The algorithm was written both using Python code with standard libraries and tensor operations of PyTorch in Python. According to experimental measurements which were realized for various batches of images, the algorithm based on tensor operations considerably reduced the computation time and provides significant accelerations over Python implementation with standard libraries.

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Author Biography

Devrim Akgun, Sakarya University, Turkey

Department of Software Engineering

Faculty of Computer and Information Sciences

Sakarya University

54050 SAKARVA

Turkey