Visual Saliency Based on Fast Nonparametric Multidimensional Entropy Estimation

Bottom-up visual saliency can be computed through information theoretic models but existing methods face significant computational challenges. Whilst nonparametric methods suffer from the curse of dimensionality problem and are computationally expensive, parametric approaches have the difficulty of determining the shape parameters of the distribution models. This paper makes two contributions to information theoretic based visual saliency models. First, we formulate visual saliency as center surround conditional entropy which gives a direct and intuitive interpretation of the center surround mechanism under the information theoretic framework. Second, and more importantly, we introduce a fast nonparametric multidimensional entropy estimation solution to make information theoretic-based saliency models computationally tractable and practicable in realtime applications. We present experimental results on publicly available eyetracking image databases to demonstrate that the proposed method is competitive to state of the art

Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396