Bottom-up saliency can be considered as a binary classification problem between centre and surround classes.

Discriminant power for classification is mutual information between image features and corresponding classes distributions.

A multi-scale structure is integrated into the framework by employing discrete wavelet features and Hidden Markov Tree (HMT).

Standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed method.

It takes less than 0.4 s to process each frame provided that MDIS is run on Intel Xenon Duo-core 2.53 GHz workstation.