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The combination of multiple features in a segmentation system is not a straightforward process.
These evaluation criteria compute some statistics for each region or class in a segmentation result.
NumPy "one-liners" include cropping or downsampling an image, or retrieving pixels corresponding to a given label in a segmentation.
Complementary to the intra-region uniformity, Levine and Nazif [15] defined a disparity measurement between two regions to evaluate the dissimilarity of regions in a segmentation result.
These results clearly show that a smoothing stage to avoid sudden changes in the decision sequence is needed in a segmentation task.
The proposed feature as a stand-alone speech/music discriminator in a segmentation system achieves an overall accuracy of over 94% on radio broadcast material.
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On the other hand, the compensation matrix had a bad behavior in a segmentation-by-classification system for the music class due to the different nature of the rest of the classes.
Early graph-based methods [24] use fixed thresholds and local measures in finding a segmentation.
In particular, a segmentation technique is introduced for partitioning a surface into regions characterized by homogeneous local texture properties.
In [17], a segmentation algorithm was proposed, along with the corresponding hardware architecture, mainly based on a connected component analysis of the binary difference image.
In [49], a segmentation system was proposed with five class location vectors (one vector per class) and a single compensation matrix U for all the classes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com