  Bottom-up saliency, an early human visual processing, behaves like binary
classification of interest and null hypothesis. Its discriminant power, mutual
information of image features and class distribution, is closely related to
saliency value by the well-known centre-surround theory. As classification
accuracy very much depends on window sizes, the discriminant saliency (power)
varies according to sampling scales. Discriminating power estimation in
multi-scales framework needs integrating with wavelet transformation and then
estimating statistical discrepancy of two consecutive scales (centre-surround
windows) by Hidden Markov Tree (HMT) model. Finally, multi-scale discriminant
saliency (MDIS) maps are combined by the maximum information rule to synthesize
a final saliency map. All MDIS maps are evaluated with standard quantitative
tools (NSS,LCC,AUC) on N.Bruce's database with ground truth data as
eye-tracking locations ; as well assessed qualitatively by visual examination
of individual cases. For evaluating MDIS against well-known AIM saliency
method, simulations are needed and described in details with several
interesting conclusions, drawn for further research directions.
