This paper decomposes the algorithmic parameters that affect the accuracy and parallel run times of mean shift segmentation. Following Comaniciu and Meer , 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.
|Title of host publication||2018 25th IEEE International Conference on Image Processing (ICIP)|
|Number of pages||5|
|Publication status||Published - 6 Sep 2018|
|Event||25th IEEE International Conference on Image Processing 2018 - Athens, Greece|
Duration: 7 Oct 2018 → 10 Oct 2018
|Conference||25th IEEE International Conference on Image Processing 2018|
|Abbreviated title||IEEE ICIP 2018|
|Period||7/10/18 → 10/10/18|
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