Rotation invariant object recognition using Gabor filters

In recent years, Gabor filters have found effective for feature extraction as they possess many properties such as tunable to specific orientation, spectrally localized, spatially localized etc. In this paper, a rotation invariant object recognition system is proposed using Gabor filters. A set of Gabor filters are considered and directional features are extracted from an image. A Gabor Vector Set is created from an unknown image sample, which may be rotated. A combined classification approach using K-Nearest Neighbor classifier and Minimum distance classifier is developed to predict the class label of the unknown sample. Experiments are conducted on electric component images which are rotated between 0° to 360° angle. An overall recognition rate of 96.02% is observed on database of size 3971 images.