Wavelet Transform Based Texture Features For Content Based Image Retrieval

The rapid expansion of the Internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. The challenge in image retrieval is to develop methods that can capture the important characteristics of an image, which makes it unique, and allow its accurate identification. The focus of this paper is on the image processing aspects that in particular using texture information for retrieval. We present a unique wavelet transform based texture features for content-based image retrieval, which is comparable with standard existing methods. We propose the use of pyramidal and tree structured wavelet features using 8-tap Daubechies coefficients for texture analysis and provide extensive experimental evaluation. Comparison with various features using Broadtz texture database indicates that the combination of energy and standard deviation of wavelet features provide good pattern retrieval accuracy for tree structured wavelet decomposition while standard deviation alone gives better result in pyramidal wavelet decomposition. In most of the existing retrieval methods the most commonly used Euclidean distance function or measure of dissimilarity between feature vectors is used, but we observed that, it is not always the best metric. We have done the comparison of results using Euclidean distance and Manhattan distance for both the tree structured and pyramidal wavelet decomposition methods and found that Manhattan distance gives better result than Euclidean distance metric.