Compressed Video Learning: Creating Active Learners

A Compressed Video Database Structured for Active Browsing and Search

We describe a unique system called ViBE (video browsing environment) for browsing and searching large databases of video sequences. The system first computes the DC sequence for a given MPEG sequence. It then detects and identifies shot boundaries by using the generalized trace. A hierarchical tree structure is constructed for shot comparison and keyframe extraction. In addition to low-level im...

Active Learning for Video Annotation

In this paper, we present an approach to active learning for video annotation. We use active learning to aide in the semantic labeling of video databases. A software library and simulator have been developed as a tool to investigate and measure the results of active learning. A list of confidence values and nearest neighbor values is maintained for each video segment in the database. A variety ...

A Model for Active Techniques for Compressed Video Transmission

Video Abstraction in H.264/AVC Compressed Domain

Video abstraction allows searching, browsing and evaluating videos only by accessing the useful contents. Most of the studies are using pixel domain, which requires the decoding process and needs more time and process consuming than compressed domain video abstraction. In this paper, we present a new video abstraction method in H.264/AVC compressed domain, AVAIF. The method is based on the norm...

Video-Based Learning Adoption: A Typology of Learners

This work builds on complexity theory in order to identify different types of learners that use video-based learning (VBL). VBL has great value as an educational tool which has already been identified in various contexts. This study combines learners’ demographics with learners’ experience in a conceptual model to explain the adoption of VBL technologies. We test and validate the proposed model...