2013 International Conference on Recent Trends in Information Technology (ICRTIT) ISBN:978-1-4799-1024-3/13/$31.00 ©2013 IEEE 137 Analysis of Biologically Inspired Model for Object Recognition S.Arivazhagan, R.Newlin Shebiah, P.Sophia, A.Nivetha Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi - 626 005 Abstract: 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. This paper presents a biologically inspired model which gives a promising solution to object categorization in color space. Here, the biologically inspired features were extracted by log-polar Gabor Transform, aided by maximum operation and convolution with Prototype patches based on the saliency of the image. The extracted features are classified by SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI) and the results are presented. Keywords: Object Recognition, Log- Gabor Transform, Biologically Inspired Model, SVM I Introduction Object recognition plays an important role in car number plate recognition[1], face recognition for the purpose of access control [2] and cancer recognition [3], applications related to computer vision such as video surveillance [4], image and video retrieval [5], web content analysis [6], human computer interactions [7] and biometrics[8] . In general, object categorization is a difficult task in computer vision because of the variability in illumination, scales, rotation, deformation and clutter as well as the complexity and variety of backgrounds. W. Niblack et.al [9] proposed traditional appearance- based approaches in the object recognition which mainly use global low-level visual features such as gray value, color, shape, and texture [9]. These methods do not consider local discriminative information and are sensitive to lighting conditions, object poses, clutter, and occlusions. J. Amores et.al. proposed Part-based models [10] that make matches between particular patches and interesting objects through various searching schemes. In this framework, it is challenging to robustly segment and find the meaningful parts, so the spatial relationships of meaningful parts cannot be duly modeled. G. Csurka, et.al introduced the original bag-of-features based scheme [11] which is efficient for recognition, but it ignores the spatial relationship of features, and thus it is hard to represent the geometric structure of the object class or to distinguish between foreground and background features. D. G. Lowe extracted Distinctive image features from scale-invariant key-points. This is a local feature based approach that combines the interest point detectors and local descriptors with spatial information. Representative local features include scale-invariant feature transform (SIFT) [12]. Although these features are effective in describing local discriminative information, they lack higher level information, e.g., relations of local orientations. T. Serre et. al. in [13] used a set of complex biologically inspired features obtained by combining the response of local edge-detectors that are slightly position- and scale-tolerant over neighboring positions and multiple orientations. T. Serre et.al in [14] proposed a new set of scale and position-tolerant feature detectors that are adaptive to the training set. This approach demonstrates good classification results on a challenging (street) scene understanding application. Jim Mutch and David G. Lowe in [15] builds on the approach of [14] by incorporating some additional biologically-motivated properties, including sparsification of features, lateral inhibition, and feature localization. J. Mutch and D. G. Lowe in [16] updates and extends the approach of [15] by incorporating some additional biologically motivated properties, specifically, sparsity and localized intermediate-level features. This paper is structured as follows: Section 2 describes about the proposed methodology. Results and discussion is given in section 3. Last section gives the concluding remarks.