Toward a perceptual object recognition system

Abstract

Advisor/s: Vincent Courboulay and Arnaud Revel. Date and location of PhD thesis defense: 5 September 2014, University of La Rochelle

The main objective of this thesis is to propose a pipeline for an object recognition algorithm based on human perception which addresses the object recognition system complexity: query run time and memory allocation. In this context, we propose a filter based on a visual attention system to address the problems of extracting a large number of points of interest using existing region selection techniques. We chose to use bottom-up visual attention systems that encode attentional fixations in a topographic map, known as a saliency map. This map serves as basis for generating a mask to select salient points according to human interest, from the points extracted by a region selection technique. Furthermore, we addressed the problem of high dimensionality of descriptors in region appearance phase. We proposed a new hybrid descriptor representing the spatial frequency of some perceptual features, extracted by a visual attention system (color, texture, intensity. This descriptor consist of a concatenation of energy measures computed at the output of a filter bank, at each level of the multi-resolution pyramid of perceptual features. This descriptor has the advantage of being lower dimensional than traditional descriptors.