Local Energy Variability as a Generic Measure of Bottom-Up Salience

Abstract

In image analysis, complexity reduction by selection of regions of interest is considered a biologically inspired strategy. In fact, Human Visual System (HVS) is constantly moving away less relevant information in favour of the most salient objects or features, by means of highly selective mechanisms forming an overall operation referred to as visual attention. This is the evolutionary solution to the well known complexity reduction problem (Tsotsos, 2005), when dealing with the processing and interpretation of natural images; a problem that is a major challenge for technical systems devoted to the processing of images or video sequences in real time. Hence, attention seems to be an adequate bio-inspired solution which can be applied in a variety of computing problems. Along with available technical advances, this fact is key to explain why the description and computational modelling of the attentional function of the HVS has experienced an enormous increase in the last two decades. In fact, applications of computing visual conspicuity are already found in many different fields: image segmentation and object learning and recognition (Rutishauser et al., 2004); vision system for robots (Witkowski & Randell, 2004) and humanoid robots (Orabona et al., 2005); visual behaviour generation in virtual human animation (Peters & O'Sullivan, 2003); processing data from 3D laser scanner (Frintrop et al., 2003); content-based image retrieval (Marques et al., 2003), etc. In models of attention it is common to differentiate between two types of attention, the bottom-up from an image-based saliency, which accounts for features that stand out from the context, and the top-down attention as task-dependent and knowledge-based. These two kinds of attention are widely assumed to interact each other, delivering a global measure of saliency that drives visual selection. In fact, neurophysiological results suggest that these two mechanisms of attention take place in separate brain areas which interact in a visual task (Corbetta & Shulman, 2002) (Buschman & Miller 2007). Regarding bottom-up attention, there are both psychophysical and neurophysiological experiments supporting the existence of some kind of an image-based saliency map in the brain, and it can be also argued that understanding of bottom-up saliency should definitely help to elucidate the mechanisms of attention (Zhaoping, 2005). Moreover, from a technical point of view, mainly concerned with a generic approach to active vision tasks, the modelling of bottom-up component of attention can play a crucial role in the reduction of the amount of information to process, regardless of the knowledge O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m