Mr. Jean-Yves Bouguet Profile

Jean-Yves Bouguet

at Intel Corp

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Proceedings Article | 29 January 2007 Paper

A spatiotemporal decomposition strategy for personal home video management

Haoran Yi, Igor Kozintsev, Marzia Polito, Yi Wu, Jean-Yves Bouguet, Ara Nefian, Carole Dulong

Proceedings Volume 6506, 65060Q (2007) https://doi.org/10.1117/12.704599

KEYWORDS: Video, Feature extraction, Image retrieval, Image segmentation, Video surveillance, Sensors, Motion measurement, Cameras, Digital video recorders, Multimedia

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With the advent and proliferation of low cost and high performance digital video recorder devices, an increasing number of personal home video clips are recorded and stored by the consumers. Compared to image data, video data is lager in size and richer in multimedia content. Efficient access to video content is expected to be more challenging than image mining. Previously, we have developed a content-based image retrieval system and the benchmarking framework for personal images. In this paper, we extend our personal image retrieval system to include personal home video clips. A possible initial solution to video mining is to represent video clips by a set of key frames extracted from them thus converting the problem into an image search one. Here we report that a careful selection of key frames may improve the retrieval accuracy. However, because video also has temporal dimension, its key frame representation is inherently limited. The use of temporal information can give us better representation for video content at semantic object and concept levels than image-only based representation. In this paper we propose a bottom-up framework to combine interest point tracking, image segmentation and motion-shape factorization to decompose the video into spatiotemporal regions. We show an example application of activity concept detection using the trajectories extracted from the spatio-temporal regions. The proposed approach shows good potential for concise representation and indexing of objects and their motion in real-life consumer video.

Proceedings Article | 16 January 2006 Paper

Requirements for benchmarking personal image retrieval systems

Jean-Yves Bouguet, Carole Dulong, Igor Kozintsev, Yi Wu

Proceedings Volume 6061, 606101 (2006) https://doi.org/10.1117/12.660254

KEYWORDS: Databases, Image retrieval, Computing systems, Image processing, Performance modeling, Feature extraction, Data modeling, Visualization, Image fusion, Digital imaging

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It is now common to have accumulated tens of thousands of personal ictures. Efficient access to that many pictures can only be done with a robust image retrieval system. This application is of high interest to Intel processor architects. It is highly compute intensive, and could motivate end users to upgrade their personal computers to the next generations of processors. A key question is how to assess the robustness of a personal image retrieval system. Personal image databases are very different from digital libraries that have been used by many Content Based Image Retrieval Systems.¹ For example a personal image database has a lot of pictures of people, but a small set of different people typically family, relatives, and friends. Pictures are taken in a limited set of places like home, work, school, and vacation destination. The most frequent queries are searched for people, and for places. These attributes, and many others affect how a personal image retrieval system should be benchmarked, and benchmarks need to be different from existing ones based on art images, or medical images for examples. The attributes of the data set do not change the list of components needed for the benchmarking of such systems as specified in²: - data sets - query tasks - ground truth - evaluation measures - benchmarking events. This paper proposed a way to build these components to be representative of personal image databases, and of the corresponding usage models.

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