Integration of complex wavelet transform and Zernike moment for multi-class classification

Abstract

Multiclass object classification is a crucial problem in computer vision research and have different emerging applications such as video surveillance. The task of multiclass object classification has more challenges because of highly variable nature and real time processing requirement of data. For tackling the multiclass object classification task, several existing methods adopt one feature or combination of features to classify objects. In this work, we propose a new combination of features-based algorithm for object classification. In the combination, the two features: (1) Daubechies complex wavelet transform (DCxWT) and (2) Zernike moments (ZM) have been used. The shift-invariance and symmetry properties of DCxWT facilitate the object classification in the wavelet domain. Specifically, the shift-invariance property of DCxWT is effective for translated object representation whereas the symmetry property yields perfect reconstruction for retaining object boundaries (i.e., edges). Moreover, translation and rotation-invariance properties of ZM are especially beneficial for the representation of varying pose and orientation of the objects. For these reasons, the composite of the two features brings about significant synthesized benefits over each single feature and the other widely used features. The multi-class support vector machine classifier is used for classifying different objects. The proposed method has been tested on standard datasets as well as our own dataset prepared by authors of this paper. Experimental results demonstrated the significant outperformance of the proposed method through quantitative evaluations and also suggest that the proposed hybridization of features is preferable for the classification problem.