Trade-off analysis between parallelism and accuracy of SLIC on apache spark

Abstract

In this paper, we present a parallel algorithm for SLIC on Apache Spark, which we call PSLIC-on-Spark. To this purpose, we have extended the original SLIC algorithm to use the operations in Apache Spark, supporting its parallel processing on multiple executors in the Apache Spark cluster. Then, we analyze the trade-off relationship of PSLIC-on-Spark between its processing speed and accuracy due to partitioning of the original image data sets. Especially, we identify two limitations in PSLIC-on-Spark, which degrade the accuracy of the original SLIC. Through experiments, we verify the trade-off relationship. Specifically, we show that PSLIC-on-Spark using 8 CPU cores reduces the processing time of SLIC by 2. 24∼2.93 times while it reduces the boundary recall (BR) of SLIC by 1. 54∼6.32 % and increases under-segmentation error (UE) by 1. 79∼6.2 %. In contrast, PSLIC-on-Spark using 2 CPU cores reduces the processing time of SLIC by 1.38∼1.45 times while it reduces the BR of SLIC by 0. 28∼1.5 %, and increases UE by 0. 25∼1.77 %. We also verify the effectiveness of PSLIC-on-Spark to deal with a large-scale image by showing that the processing speed of PSLIC-on-Spark becomes much more efficient as the image size becomes large. Specifically, compared to the original SLIC, the proposed SLIC-on-Spark reduces its processing time by 2.23 times for the image of 480×320 pixels and by 5.59 times for the image of 2002×1335 pixels, respectively