Texture Image Retrieval Based On P-Contourlet

With the rapid growth of the digital image in people’s lives, how to search for a specific image from a large of images dataset becomes a hot and difficult question. Texture is an important feature of natural images, it can engender both psychological and visual impact. Signal processing based texture analysis method is established on the time-frequency analysis and multiresolution analysis. Psychophysical studies show that the brain will analysis the frequency components of images when people observe it. So signal processing based texture analysis method is fit human’s visual and psychological feelings.This paper mainly studies on signal processing based texture image retrieval. Under the premise of large number experiments and detailed studies on traditional multi-resolution analysis based texture retrieval, we propose a new projection-based contourlet transform-P-Contourlet transform. Which is multi-resolution, multi-directional selectivity, nearly shift invariance, rich phase information and low redundancy. The main work is as follows:1. Describes the background and research significance of image retrieval. Review some major research results and development trends in this filed. Make a brief summary about texture based image retrieval methods in presently.2. Makes a detail studies on wavelet transform, dual-tree complex wavelet transform, Contourlet transform, Nonsubsampled Contourlet Transform and PDTDFB transform, which was widely popular in present. Apply these transforms to texture image retrieval and do a large number experimental analysis.3. Presents a new shift invariant complex contourlet transform—P-Contourlet transform. Which is multi-resolution, multi-directional selectivity, nearly shift invariance, rich phase information and low redundancy. Apply it to texture image retrieval and the experiments shows it’s efficient.4. Presents a new texture image retrieval method based on P-Contourlet transform and Hidden Markov Tree (HMT) model. By modeling the P-Contourlet transform’s each subbands coefficients using hidden Markov tree, obtains the images texture feature vectors. The experiments shows that HMT model is validity in modeling P-Contourlet transform’s each subbands coefficients and this retrieval method is efficiency.