Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Tracking Using Level Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
Video shot boundary detection: Seven years of TRECVid activity
Computer Vision and Image Understanding
Video Object Mining: Issues and Perspectives
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
A survey of semantic image and video annotation tools
Knowledge-driven multimedia information extraction and ontology evolution
Annotation based personalized adaptation and presentation of videos for mobile applications
Multimedia Tools and Applications
IEEE Transactions on Image Processing
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A vital prerequisite for fine-grained video content processing (indexing, querying, retrieval, adaptation, etc.) is the production of accurate metadata describing its structure and semantics. Several annotation tools were presented in the literature generating metadata at different granularities (i.e. scenes, shots, frames, objects). These tools have a number of limitations with respect to the annotation of objects. Though they provide functionalities to localize and annotate an object in a frame, the propagation of this information in the next frames still requires human intervention. Furthermore, they are based on video models that lack expressiveness along the spatial and semantic dimensions. To address these shortcomings, we propose the Semantic Video Content Annotation Tool (SVCAT) for structural and high-level semantic annotation. SVCAT is a semi-automatic annotation tool compliant with the MPEG-7 standard, which produces metadata according to an object-based video content model described in this paper. In particular, the novelty of SVCAT lies in its automatic propagation of the object localization and description metadata realized by tracking their contour through the video, thus drastically alleviating the task of the annotator. Experimental results show that SVCAT provides accurate metadata to object-based applications, particularly exact contours of multiple deformable objects.