Colour Object Recognition using Biologically Inspired Model

Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. Utilization of characteristics of biological systems for solving practical problems inevitably leads towards reducing the gap between manmade machines and live systems. Biologically motivated information processing has been an important area of scientific research for decades. This paper presents a Biologically Inspired Model which gives a promising solution to object categorization in colour space. The features are extracted in YCbCr colour space and classified by using SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI). The proposed framework can successfully detect and classify the object categories with a good accuracy rate of about 91.3% for the Cb plane.