Image classification using wavelet coefficients for video surveillance

In this paper a new approach of wavelet coefficient features to identify and classify different images encountered by video surveillance systems is introduced. The main objective is to identify specific wavelet filters for achieving a better classification of images. The fundamental properties of wavelet coefficients for feature selection for image classification are investigated. Haar, Daubechies 5, Symlet 2, and Biorthogonal 2.2 wavelets have been used in this investigation. The results show that Haar wavelet provides promising results in object retrieval compared to Daubechies 5, Symlet 2, and Biorthogonal 2.2 wavelets.