15,788 research outputs found
Hierarchical modelling and adaptive clustering for real-time summarization of rush videos
In this paper, we provide detailed descriptions of a proposed new algorithm for video summarization, which are also included in our submission to TRECVID'08 on BBC rush summarization. Firstly, rush videos are hierarchically modeled using the formal language technique. Secondly, shot detections are applied to introduce a new concept of V-unit for structuring videos in line with the hierarchical model, and thus junk frames within the model are effectively removed. Thirdly, adaptive clustering is employed to group shots into clusters to determine retakes for redundancy removal. Finally, each most representative shot selected from every cluster is ranked according to its length and sum of activity level for summarization. Competitive results have been achieved to prove the effectiveness and efficiency of our techniques, which are fully implemented in the compressed domain. Our work does not require high-level semantics such as object detection and speech/audio analysis which provides a more flexible and general solution for this topic
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
PEEK: A Large Dataset of Learner Engagement with Educational Videos
Educational recommenders have received much less attention in comparison to
e-commerce and entertainment-related recommenders, even though efficient
intelligent tutors have great potential to improve learning gains. One of the
main challenges in advancing this research direction is the scarcity of large,
publicly available datasets. In this work, we release a large, novel dataset of
learners engaging with educational videos in-the-wild. The dataset, named
Personalised Educational Engagement with Knowledge Topics PEEK, is the first
publicly available dataset of this nature. The video lectures have been
associated with Wikipedia concepts related to the material of the lecture, thus
providing a humanly intuitive taxonomy. We believe that granular learner
engagement signals in unison with rich content representations will pave the
way to building powerful personalization algorithms that will revolutionise
educational and informational recommendation systems. Towards this goal, we 1)
construct a novel dataset from a popular video lecture repository, 2) identify
a set of benchmark algorithms to model engagement, and 3) run extensive
experimentation on the PEEK dataset to demonstrate its value. Our experiments
with the dataset show promise in building powerful informational recommender
systems. The dataset and the support code is available publicly
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