The TREC2001 video track: information retrieval on digital video information

The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable

Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD's IMF.

In this paper, we address the problem of medical ddiagnosis aid through content based image retrieval methods. We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Intrinsic Mode Functions (IMF) and a residu. The generalized Gaussian density function (GGD) is used to represent the coefficients derived from each IMF, and the Kullback–Leibler Distance (KLD) compute the similarity between GGDs. Retrieval efficiency is given for different databases including a diabetic retinopathy, and a face database. Results are promising: the retrieval efficiency is higher than 85% for some cases

Evaluation of Handwriting Similarities Using Hermite Transform

http://www.suvisoft.comIn this paper, we present a new method for handwriting documents denoising and indexing. This work is based on the Hermite Transform, which is a polynomial transform and a good model of the human visual system (HVS). We use this transformation to analyze handwritings using their visual aspect of texture. We apply this analysis to document indexing (finding documents coming from the same author) or document classification (grouping document containing handwritings that have similar visual aspect). It is often necessary to clean these documents before the analyze step. For that purpose, we use also the Hermite decomposition. The current results are very promising and show that it is possible to characterize handwritten drawings without any a priori graphemes segmentation