Information theoretical region merging techniques have been shown
to provide a state-of-the-art unified solution for natural and texture
image segmentation. Here, we study how the segmentation results
can be further improved by a more accurate estimation of the statistical
model characterizing the regions. Concretely, we explore four
density estimators that can be used for pdf or joint pdf estimation.
The first three are based on different quantization strategies: a general
uniform quantization, an MDL-based uniform quantization, and
a data-dependent partitioning and estimation. The fourth strategy is
based on a computationally efficient kernel-based estimator (averaged
shifted histogram). Finally, all estimators are objectively evaluated
using a database with available ground truth partitions.
CitationCalderero, F.; Marques, F.; Ortega, A. Performance evaluation of probability density estimators for unsupervised information theoretical region merging. A: International Conference on Image Processing. "16th International Conference on Image Processing". 2009, p. 4397-4400.
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