Segmentation and structuring of video documents for indexing applications

• Main Author: Tapu, Ruxandra Georgina
• Language: ENG
• Published: Institut National des Télécommunications 2012
• Subjects: [SHS:ECO] Humanities and Social Sciences/Economies and finances; [INFO:INFO_OH] Computer Science/Other; Video transition detection; Graph partition; Multi-resolution analysis; Automatic abstraction; High level semantic segmentation; Salient object detection and segmentation
• Online Access: http://tel.archives-ouvertes.fr/tel-00843596; http://tel.archives-ouvertes.fr/docs/00/84/35/96/PDF/ThA_se_TapuRuxandra.pdf

Recent advances in telecommunications, collaborated with the development of image and video processing and acquisition devices has lead to a spectacular growth of the amount of the visual content data stored, transmitted and exchanged over Internet. Within this context, elaborating efficient tools to access, browse and retrieve video content has become a crucial challenge. In Chapter 2 we introduce and validate a novel shot boundary detection algorithm able to identify abrupt and gradual transitions. The technique is based on an enhanced graph partition model, combined with a multi-resolution analysis and a non-linear filtering operation. The global computational complexity is reduced by implementing a two-pass approach strategy. In Chapter 3 the video abstraction problem is considered. In our case, we have developed a keyframe representation system that extracts a variable number of images from each detected shot, depending on the visual content variation. The Chapter 4 deals with the issue of high level semantic segmentation into scenes. Here, a novel scene/DVD chapter detection method is introduced and validated. Spatio-temporal coherent shots are clustered into the same scene based on a set of temporal constraints, adaptive thresholds and neutralized shots. Chapter 5 considers the issue of object detection and segmentation. Here we introduce a novel spatio-temporal visual saliency system based on: region contrast, interest points correspondence, geometric transforms, motion classes' estimation and regions temporal consistency. The proposed technique is extended on 3D videos by representing the stereoscopic perception as a 2D video and its associated depth