Image epitome generation and its applications

Abstract

An image modeling scheme named “Epitome" was proposed by Brendan J. Frey and Nebjsa Jojic in 2002 [1]. Image epitome is a miniature, condensed version of the image. It is much smaller in the size compared to the input image but it contains the most constituent elements representing the image.

Epitome has been used in various Image processing applications. It has been used for the Image processing applications like Image compression, Image denoising, Parts-based image retrieval, Image segmentation and Image in-painting.

The estimation process of the epitome needs to be studied. The method proposed in [1] uses EM algorithm for epitome estimation. Instead of EM, some other estimation techniques can be applied to Epitome Generation. This modification to Epitome generation scheme may build better epitomes for different applications of Image epitome. It may happen that one epitome generation method generates epitomes which are more suitable for one application while less suitable for another application.

In this thesis, a modification to the method proposed in [1] for Image denoising using epitome is proposed. This method gives a significant amount of improvement in the Image denoising. Also, two different parameter estimation techniques named DAEM (Deterministic Annealing EM) and Bayesian Sampling-Resampling approach are studied and applied for epitome generation. These methods are experimented for Image Reconstruction and Image denoising. DAEM gives improvement in Image denoising and Bayesian Learning based approach gives significant improvement in Image Reconstruction.