Stéphane Marchand-Maillet

Bio: Stéphane Marchand-Maillet is an academic researcher from University of Geneva. The author has contributed to research in topics: Image retrieval & Search engine indexing. The author has an hindex of 21, co-authored 202 publications receiving 2843 citations. Previous affiliations of Stéphane Marchand-Maillet include Institut Eurécom & Geneva College.

Second Generation Benchmarking and Application Oriented Evaluation

Abstract: Digital image watermarking techniques for copyright protection have become increasingly robust. The best algorithms perform well against the now standard benchmark tests included in the Stirmark package. However the stirmark tests are limited since in general they do not properly model the watermarking process and consequently are limited in their potential to removing the best watermarks. Here we propose a second generation benchmark for image watermarking which includes attacks which take into account powerful prior information about the watermark and the watermarking algorithms. We follow the model of the Stirmark benchmark and propose several new categories of tests including: denoising (ML and MAP), wavelet compression, watermark copy attack, active desynchronization, denoising, geometrical attacks, and denoising followed by perceptual remodulation. In addition, we take the important step of presenting results as a function of application. This is an important contribution since it is unlikely that one technology will be suitable for all applications.

The Truth about Corel - Evaluation in Image Retrieval

Abstract: To demonstrate the performance of content-based image retrieval systems (CBIRSs), there is not yet any standard data set that is widely used. The only dataset used by a large number of research groups are the Corel Photo CDs. There are more than 800 of those CDs, each containing 100 pictures roughly similar in theme. Unfortunately, basically every evaluation is done on a different subset of the image sets thus making comparison impossible.In this article, we compare different ways of evaluating the performance using a subset of the Corel images with the same CBIRSan d the same set of evaluation measures. The aim is to show how easy it is to get differing results, even when using the same image collection, the same CBIRS and the same performance measures. This pinpoints the fact that we need a standard database of images with a query set and corresponding relevance judgments (RJs) to really compare systems.The techniques used in this article to "enhance" the apparent performance of a CBIRSa re commonly used, sometimes described, sometimes not. They all have a justification and seem to change the performance of a CBIRS but they do actually not. With a larger subset of images it is of course much easier to generate even bigger differences in performance. The goal of this article is not to be a guide of how to make the "apparent" performance of systems look good, but rather to make readers aware of CBIRS evaluations and the importance of standardized image databases, queries and RJ.

Strategies for positive and negative relevance feedback in image retrieval

Abstract: Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. It has also been increasingly used in content-based image retrieval and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings. This paper compares a variety of strategies for positive and negative feedback. The performance evaluation of feedback algorithms is a hard problem. To solve this, we obtain judgments from several users and employ an automated feedback scheme. We then evaluate different techniques using the same judgements. Using automated feedback, the ability of a system to adapt to the user's needs can be measured very effectively. Our study highlights the utility of negative feedback, especially over several feedback steps.

Binary Digital Image Processing: A Discrete Approach

Abstract: Foreword. Acknowledgements. Notation. Preface. Digital topology. Discrete geometry. Algorithmic graph theory. Acquisition and storage. Distance transformations. Binary digital image characteristics. Image thinning. Some applications. References. Index.

Video Google: a text retrieval approach to object matching in videos

Abstract: We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films.

Image retrieval: Ideas, influences, and trends of the new age

Abstract: We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

Digital Watermarking

Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.