Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

Presents the theory, design principles, implementation and performance results of PicHunter, a prototype content-based image retrieval (CBIR) system. In addition, this document presents the rationale, design and results of psychophysical experiments that were conducted to address some key issues that arose during PicHunter's development. The PicHunter project makes four primary contributions to research on CBIR. First, PicHunter represents a simple instance of a general Bayesian framework which we describe for using relevance feedback to direct a search. With an explicit model of what users would do, given the target image they want, PicHunter uses Bayes's rule to predict the target they want, given their actions. This is done via a probability distribution over possible image targets, rather than by refining a query. Second, an entropy-minimizing display algorithm is described that attempts to maximize the information obtained from a user at each iteration of the search. Third, PicHunter makes use of hidden annotation rather than a possibly inaccurate/inconsistent annotation structure that the user must learn and make queries in. Finally, PicHunter introduces two experimental paradigms to quantitatively evaluate the performance of the system, and psychophysical experiments are presented that support the theoretical claims.

/pdf/the-bayesian-image-retrieval-system-pichunter-theory-4rqjnp3alm.pdf

The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments

ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases

This paper presents a new direct Fourier-based algorithm for performing image-to-image registration to subpixel accuracy, where the image differences are restricted to translations and uniform changes of illumination. The algorithm detects the Fourier components that have become unreliable estimators of shift due to aliasing, and removes them from the shift-estimate computation. In the presence of aliasing, the average precision of the registration is a few hundredths of a pixel. Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of P. Thevenaz et al. (1998).

/pdf/a-fast-direct-fourier-based-algorithm-for-subpixel-2a8msvkrn2.pdf

A fast direct Fourier-based algorithm for subpixel registration of images

This paper describes PicHunter, an image retrieval system that implements a novel approach to relevance feedback, such that the entire history of user selections contributes to the system's estimate of the user's goal image. To accomplish this, PicHunter uses Bayesian learning based on a probabilistic model of a user's behavior. The predictions of this model are combined with the selections made during a search to estimate the probability associated with each image. These probabilities are then used to select images for display. Details of our model of a user's behavior were tuned using an off-line leaning algorithm. For clarity, our studies were done with the simplest possible user interface but the algorithm can easily be incorporated into systems which support complex queries, including most previously proposed systems. However, even with this constraint and simple image features, PicHunter is able to locate randomly selected targets in a database of 4522 images after displaying an average of only 55 groups of 4 images which is over 10 times better than chance. We therefore expect that the performance of current image database retrieval systems can be improved by incorporation of the techniques described here.

/pdf/pichunter-bayesian-relevance-feedback-for-image-retrieval-3yyh708dls.pdf

PicHunter: Bayesian relevance feedback for image retrieval

This paper derives a multiresolution analysis technique for performing correlations on wavelet representations of images. The technique maps the images into the wavelet-frequency domain to take advantage of high-speed correlation in the frequency domain. It builds on Vaidyanathan's (1993) wavelet correlation theorem, which shows that subsamples of correlations of two signals can be obtained from a sum of correlations of subbands of wavelet representations of those signals. Our algorithm produces the correlations at lowest resolution by applying the convolution theorem to subband correlations. A new multiresolution technique fills in the missing correlation data by incrementally inverting the wavelet transform and refining the Fourier transform. When applied to JPEG representations of data, the lowest resolution correlations can he performed directly on the JPEG images to produce 1/64th of the correlation points. Each of three incremental steps quadruple the number of correlation points, and the process can be halted at any point if the intermediate results indicate that the correlation will not find a match.

Progressive wavelet correlation using Fourier methods

This paper describes an algorithm for searching image databases for images that match a specified pattern. The application in mind for this algorithm is a query system for a large library of digitized satellite images. The algorithm has two thresholds that allow the user to adjust independently the closeness of a match. One threshold controls an intensity match and the other controls a texture match. The thresholds are correlations that can be computed efficiently in the Fourier transform domain of an image, and are particularly efficient to compute when the Fourier coefficients are mostly zero. Thus the scheme works well with image-compression algorithms that replace small Fourier coefficients by zeros. For compressed images, the majority of the cost of processing such images is in computing the inverse transforms plus a few operations per pixel for nonlinear threshold operations. The quality of retrieval for this algorithm has not been evaluated at this writing. We show the use of this technique on a typical satellite image. The technique may be suitable for automatic identification of cloud-free images, for making crude classifications of land use, and for finding isolated features that have unique intensity and texture characteristics. We discuss how to generalize the algorithm from matching gray-scale intensity to color or multispectral images.

Image matching by means of intensity and texture matching in the Fourier domain

This paper describes and compares image registration techniques for situations in which one or both candidate images are partially occluded. Both fractional masks (introduced) and binary masks improve registration accuracy. Efficient mask-based algorithms operate at low resolution on low-pass subbands of wavelets and correlate images in the frequency domain.

Techniques for multiresolution image registration in the presence of occlusions

This correspondence explores the existence of convolution theorem for linear transformations under a variety of different assumptions. There are eight convolution theorems, all Fourier-related with only N operations in the transform domain and no ordering constraints on the convolution components in the result. They include circular convolutions and correlations.

H.S. Stone

Papers

Progressive wavelet correlation using Fourier methods

Image matching by means of intensity and texture matching in the Fourier domain

Techniques for multiresolution image registration in the presence of occlusions

Convolution theorems for linear transforms

The use of image content to control image retrieval and image processing