Parallel Mean Shift Accuracy and Performance Trade-Offs

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This paper decomposes the algorithmic parameters that affect the accuracy and parallel run times of mean shift segmentation. Following Comaniciu and Meer [1], rather than perform calculations in the feature space of the image, the joint spatial-range domain is represented by the image space, with feature space information associated with each point. We report parallel speedup and segmentation accuracy using a standardised segmentation dataset and the Probabilistic Rand index (PRI) accuracy measure. Changes to the algorithmic parameters are analysed and a sweet spot between PRI and run time is found. Using a range window radius of 20, spatial window radius of 10 and threshold of 50, the PRI is improved by 0.17, an increase of 34% which is comparable to state of the art. Mean shift clustering run time is reduced by 97% with parallelism, a speedup of 32 on a 64-core CPU.
Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)9781479970612
Publication statusPublished - 6 Sept 2018
Event25th IEEE International Conference on Image Processing 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018


Conference25th IEEE International Conference on Image Processing 2018
Abbreviated titleIEEE ICIP 2018
Internet address


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