Analysis of biologically inspired model for object recognition

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.

Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

“…The feature map has affine invariance of rotation, scaling and translation, but it performs poorly on large affine distortion and noise. In [32], affine‐invariant features are extracted by log‐polar Gabor transform, aided by maximum operation and convolution prototype patches. The proposed method obtains better recognition performance through SVM classifier, and has affine invariance to scale, rotation and perspective.…”