Open AccessAffine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition
Author(s) -
Yu Lingli,
Xia Xumei,
Zhou Kiajun,
Zhao Lijun
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5488
Subject(s) - affine transformation , invariant (physics) , artificial intelligence , feature extraction , pattern recognition (psychology) , cognitive neuroscience of visual object recognition , computer science , fusion , computer vision , object (grammar) , feature (linguistics) , mathematics , geometry , linguistics , philosophy , mathematical physics
It is difficult to recognise an image with affine transformation due to viewing angle anddistance variations. Therefore, affine invariant feature extraction is avaluable technology in the field of image recognition. Inspired by bio‐visualmechanism, an affine invariant for object recognition method based on a fusionfeature framework is proposed in this study, which employs geometry descriptorand double biologically inspired transformation (DBIT). First, a shape featureof interest detector is adopted to detect contour features. Then, the areaestimation of affine region detector is utilised to construct area ratio featurevectors. Second, an orientation edge detector is built to highlight the edges ofdifferent directions. On this basis, local space frequency detector is adoptedto measure the spatial frequency at each direction and interval, which convertsthe output map into DBIT feature vectors. A weighted fusion strategy isperformed based on Pearson correlation distance to fuse the geometry feature andDBIT feature. Some tests for Alphanumeric, Coil‐100 MPEG‐7, Mixed NationalInstitute of Standards and Technology (MNIST) and Olivetti Research Laboratoryface images database (ORL) database remain highly stable recognition accuracy,even when the shear factor is between −0.5 and + 0.5. The experiment resultsshow the authors’ proposed approach has a nice performance in featureinvariance, selectivity and recognition accuracy.