Human Attention Modelization and Data Reduction [chapter]

Matei Mancas, Dominique De, Nicolas Riche, Xavier Siebert
2012 Video Compression  
6 2 will be set by intech Attention modeling: what is saliency? In this first part of the chapter, a global view of the methods used to model attention in computer science will be presented. The details provided here will be useful to understand the next parts of the chapter which are dedicated to attention-based image and video compression. Attention in computer science: idea and approaches There are two main approaches to attention modeling in computer science. The first
more » ... is based on the notion of "saliency" and implies a competition between "bottom-up" and "top-down" information. The idea of saliency maps is that the sight or gaze of people will direct to areas which, in some way, stand out from the background. The eye movements can be computed from the saliency map by using winner-take-all (Itti et al. (1998) ) or more dynamical algorithms (Mancas, Pirri & Pizzoli (2011) ). The second approach to attention modeling is based on the notion of "visibility" which assumes that people look to locations that will lead to successful task performance. Those models are dynamic and intend to maximize the information acquired by the eye (the visibility) of eccentric regions compared to the current eye fixation to solve a given task (which can also be free viewing). In this case top-down information is naturally included in the notion of task along with the dynamic bottom-up information maximization. The eye movements are in this approach directly an output from the model and do not have to be inferred from a saliency map. The literature about attention modeling in computer science is not symmetric between those two approaches: the saliency-based methods are much more popular than the visibility models. For this reason, the following sections in this first part of the chapter will also mainly deal with saliency methods, but a review of visibility methods will be provided in the end. Saliency approaches: bottom-up methods
doi:10.5772/34942 fatcat:esq57asj2vf27gzfir4ycx36im