Biologically Inspired Object Categorization in Cluttered Scenes

Humans have the ability to recognize objects in a cluttered scene in 100 s of milliseconds. Computer algorithms operate at a much lower performance level compared to humans. Furthermore, it has proven to be particularly difficult to develop algorithms to recognize all objects in a category, such as, all cat faces vs dog faces, because of the large in-class variability. The distinguishing features can vary significantly among different objects in the same class. A similar case can be made for other categories, such as, cars, human faces, etc. In this paper we approach this problem using a model of the human visual system. The human visual system can be divided into two major pathways, commonly called the ´what´ and ´where´ pathways. The ´what´ pathway recognizes an object in a scene, but not its specific location. In this paper we present a biologically inspired hierarchical ´what´ neural network that can successfully classify objects into categories.