  It is well-known in image processing that computational cost increases
rapidly with the number and dimensions of the images to be processed. Several
fields, such as medical imaging, routinely use numerous very large images,
which might also be 3D and/or captured at several frequency bands, all adding
to the computational expense. Multiresolution analysis is a method of
increasing the efficiency of the segmentation process. One multiresolution
approach is the coarse-to-fine segmentation strategy, whereby the segmentation
starts at a coarse resolution and is then fine-tuned during subsequent steps.
The starting resolution for segmentation is generally selected arbitrarily with
no clear selection criteria. The research reported in this paper showed that
starting from different resolutions for image segmentation results in different
accuracies and computational times, even for images of the same category
(depicting similar scenes or objects). An automated method for resolution
selection for an input image would thus be beneficial. This paper introduces a
framework for the automated selection of the best resolution for image
segmentation. We propose a measure for defining the best resolution based on
user/system criteria, offering a trade-off between accuracy and computation
time. A learning approach is then introduced for the selection of the
resolution, whereby extracted image features are mapped to the previously
determined best resolution. In the learning process, class (i.e., resolution)
distribution is generally imbalanced, making effective learning from the data
difficult. Experiments conducted with three datasets using two different
segmentation algorithms show that the resolutions selected through learning
enable much faster segmentation than the original ones, while retaining at
least the original accuracy.
