Incorporating models of human perception into the process of scene interpretation and object recognition in visual content is a strong trend in computer vision. In this paper we tackle the modeling of visual perception via automatic visual saliency maps for object recognition. Visual saliency represents an efficient way to drive the scene analysis towards particular areas considered 'of interest' for a viewer and an efficient alternative to computationally intensive sliding window methods for object recognition. Using saliency maps, we consider biologically inspired independent paths of central and peripheral vision and apply them to fundamental steps of the so-called Bag-of-Words (BoW) paradigm, such as features sampling, pooling and encoding.
perceptual modeling; visual saliency; active object recognition; foveal and peripheral pathways; classification; attention; features