A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention

Background An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and cortical magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account. Methods We propose a new framework for visual information acquisition and representation that emulates the architecture of the primate visual system by integrating features such as retinal sampling and cortical magnification while avoiding spatial deformations and other side effects produced by models that tried to implement these two features. It also explicitly integrates the notion of visual angle, which is rarely taken into account by vision models. We argue that this framework can provide the infrastructure for implementing vision tasks such as object recognition and computational visual attention algorithms. Results To demonstrate the utility of the proposed vision framework, we propose an algorithm for bottom-up saliency prediction implemented using the proposed architecture. We evaluate the performance of the proposed model on the MIT saliency benchmark and show that it attains state-of-the-art performance, while providing some advantages over other models. Conclusion Here is a summary of the main contributions of this paper: (1) Introducing a new bio-inspired framework for visual information acquisition and representation that offers the following properties: (a) Providing a method for taking the distance between an image and the viewer into account. This is done by incorporating a visual angle parameter which is ignored by most visual acquisition models. (b) Reducing the amount of visual information acquired by introducing a new scheme for emulating retinal sampling and cortical magnification effects observed in the ventral stream. (2) Providing a concrete application of the proposed framework by using it as a substrate for building a new saliency-based visual attention model, which is shown to attain state-of-the-art performance on the MIT saliency benchmark. (3) Providing an online Git repository that implements the introduced framework that is meant to be developed as a scalable, collaborative project.