Fine-granularity semantic video annotation: An approach based on automatic shot level concept detection and object recognition
Résumé
A fine-grained video content indexing, retrieval, and adaptation requires accurate metadata describing the video structure and semantics to the lowest granularity, i.e. to the object level. The authors address these requirements by proposing semantic video content annotation tool (SVCAT) for structural and high-level semantic video annotation. SVCAT is a semi-automatic MPEG-7 standard compliant annotation tool, which produces metadata according to a new object-based video content model introduced in this work. Videos are temporally segmented into shots and shots level concepts are detected automatically using ImageNet as background knowledge. These concepts are used as a guide to easily locate and select objects of interest which are then tracked automatically to generate an object level metadata. The integration of shot based concept detection with object localization and tracking drastically alleviates the task of an annotator. The paper aims to discuss these issues.