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Cosine-Modulated Wavelet Packet based Texture Features for Content based Image Retrieval

Content-based image retrieval (CBIR) is an active research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular texture features for CBIR. We propose Cosine modulated wavelet packet based texture features for CBIR. The major advantages of Cosine modulated wavelet transform are less implementation complexity and good filter quality. Texture features are obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, texture database of 1856 textures is created from Brodatz album. Results are compared with standard Daubechies wavelet packet, Af-band wavelet packet and Gabor wavelet. Result indicates that proposed method is superior to other methods in terms of retrieval accuracy and retrieval time.