Multiclass image classification using multiscale biorthogonal wavelet transform

Image classification is an important problem because of its applications in many fields like shape analysis, object tracking, image retrieval etc. Many techniques have been proposed in literature for classification of objects into two classes. Multi class image classification with high accuracy is a challenging task. In this paper we propose a new algorithm for multi class image classification that uses biorthogonal wavelet transform as image feature. Original images are decomposed into subbands LL, LH, HL and HH using Biorthogonal wavelet transform at multiple scales. The coefficients of LH, HL, HH subbands are used as features for classification. The approximate shift invariance and linear phase properties of Biorthogonal wavelet transform are useful for classification of images. Also, the lifting-scheme of Biorthogonal wavelet yields reduced computational cost. Quantitative evaluation of classification accuracy demonstrates the strength of the proposed method.