Unsupervised edge map scoring: a statistical complexity approach.

GIMENEZ, J. A.; JORGE MARTINEZ; ANA GEORGINA FLESIA

COMPUTER VISION AND IMAGE UNDERSTANDING

ACADEMIC PRESS INC ELSEVIER SCIENCE

Lugar: Amsterdam; Año: 2014 vol. 122 p. 131 - 142

1077-3142

We propose a new Statistical Complexity Measure (SCM) to qualify edge maps without Ground Truth (GT) knowledge. The measure is the product of two indices, an Equilibrium index E obtained by projecting the edge map into a family of edge patterns, and an Entropy index H, defined as a function of the Kolmogorov Smirnov (KS) statistic. This new measure can be used for performance characterization which includes: (i)~the specific evaluation of an algorithm (intra-technique process) in order to identify its best parameters, and (ii)~the comparison of different algorithms (inter-technique process) in order to classify them according to their quality. Results made over images of the South Florida and Berkeley databases show that our approach significantly improves over Pratt´s Figure of Merit (PFoM) which is the objective reference-based edge map evaluation standard, as it takes into account more features in its evaluation.