Bassem Bouaziz
Walid Mahdi
ABSTRACT
Text in videos contains much semantic information that can be used for video indexing and browsing. In this paper, we propose a spatiotemporal video-text localization and identification approach which proceeds in two main steps: text region localization and text region identification. In the first step we detect the significant appearance of the new objects in a frame by a split and merge processes applied on binarized edge frame pair differences. Detected objects are, a priori, considered as text. They are then filtered according to both local contrast and texture criteria in order to get the effective ones. The resulted text regions are identified based on a visual grammar descriptor containing a set of semantic text class regions characterized by visual features. A visual table of content is generated based on extracted text regions occurring within video sequence enriched by a semantic identification. The experimentation performed on a variety of video sequences shows the efficiency of our.
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Index Terms
- A spatiotemporal text localization and identification approach for content-based video browsing
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