Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU

Local Rank Differences, LRD, AdaBoost, Object Detection

Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend . The computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local Rank Difference is an image feature that is alternative to commonly used Haar features. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as shown in this paper, it performs very well on common CPU. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD using the multimedia instruction set of current general-purpose processors, presents its empirical performance measures compared to alternative approaches, and suggests several notes on practical usage of LRD and proposes directions for future work.