Hybrid Salient Object Extraction Approach with Automatic Estimation of Visual Attention Scale

In this work we present an intelligent approach to detection and extraction of salient objects. The described system is inspired by early processing stages of human visual system and is based on our previous work on the field of visual saliency. Building on our preceding system, which worked with a fixed visual attention scale, we develop a machine learning approach using an artificial neural network and genetic algorithm, estimating automatically the visual attention scale for each input image individually. The whole approach has low complexity and can be run in speed close to real-time on contemporary processors. Quantitative evaluation results of the described approach with visual attention scale estimation are compared to results obtained with a fixed scale and to results of two other existing salient object detection techniques. The system is a part of our work on an intelligent machine vision system, using visual saliency for unsupervised learning of objects.