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

Abstract

It is difficult to recognise an image with affine transformation due to viewing angle and distance variations. Therefore, affine invariant feature extraction is a valuable technology in the field of image recognition. Inspired by bio-visual mechanism, an affine invariant for object recognition method based on a fusion feature framework is proposed in this study, which employs geometry descriptor and double biologically inspired transformation (DBIT). First, a shape feature of interest detector is adopted to detect contour features. Then, the area estimation of affine region detector is utilised to construct area ratio feature vectors. Second, an orientation edge detector is built to highlight the edges of different directions. On this basis, local space frequency detector is adopted to measure the spatial frequency at each direction and interval, which converts the output map into DBIT feature vectors. A weighted fusion strategy is performed based on Pearson correlation distance to fuse the geometry feature and DBIT feature. Some tests for Alphanumeric, Coil-100 MPEG-7, Mixed National Institute of Standards and Technology (MNIST) and Olivetti Research Laboratory face images database (ORL) database remain highly stable recognition accuracy, even when the shear factor is between -0.5 and + 0.5. The experiment results show the authors' proposed approach has a nice performance in feature invariance, selectivity and recognition accuracy.