Our first contribution is to review, structure, and deduplicate the measures available.
We pro- vide a new measure that improves previous ones in terms of a set of qualitative and quantitative meta-measures.
For example one can segment a human face from a color video with an algorithm.
But in tough cases, such as to extract a complete road network from a grey scale aerial image, the segmentation procedure is not easy to do and might require applications of a great deal of domain a building knowledge.
We also extend the measures on flat partitions to cover hierarchical segmentations.
The third part of this Thesis moves from the evaluation of image segmentation to its application to object detection.
Second, ground-truth databases are of paramount importance in the evaluation.
They can be divided into those annotated only at object level, that is, with marked sets of pixels that refer to objects that do not cover the whole image; or those with annotated full partitions, which provide a full clustering of all pixels in an image.
Depending on the type of database, we say that the analysis is done from an object perspective or from a partition perspective.
Finally, the similarity measures used to compare the generated results to the ground truth are what will provide us with a quantitative tool to evaluate whether our results are good, and in which way they can be improved.