Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. To overcome this limitation, this paper contributes a visual recommender system called MOOCex. The system recommends lecture videos across different courses by considering both video contents and sequential inter-topic relationships mined from course syllabi; and more importantly, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context. When compared to traditional methods (e.g., content-based recommendation and ranked list representations), MOOCex suggests videos from more diverse perspectives and helps learners make better video playback decisions. Further, feedback from MOOC learners and instructors indicates that the system enhances both learning and teaching effectiveness.

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos across multiple courses and content platforms to provide a choice of perspectives on topics of interest. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.

We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments across the collection, using word vectors from the transcripts, and then select the most central segments for the summaries. We evaluate the method quantitatively on the AMI Meeting Corpus using gold standard reference summaries and the Rouge metric, and qualitatively on lecture recordings using a novel two-tiered approach with human judges. The results show that our method compares favorably with others in terms of Rouge, and outperforms the baselines for human scores, thus also validating our evaluation protocol.

In this paper, we present an approach for topic-level search and hyperlinking of video snippets, which relies on content-based recommendation and multimodal re-ranking techniques. We identify topic-level segments using transcripts or subtitles and enrich them with other metadata. Segments are indexed in a word vector space. Given a text query or an anchor, the most similar segments are retrieved using cosine similarity scores, which are then combined with visual similarity scores, computed as the distance from the anchor's visual concept vector. This approach has performed well on the MediaEval 2013 Search and Hyperlinking task, evaluated over 1260 hours of BBC TV broadcast, in terms of overall mean average precision. Experiments showed that topic-segments based on transcripts from automatic speech recognition level systems (ASR) led to better performance than the ones based on subtitles for both search and hyperlinking. Moreover, by analyzing the effect of multimodal re-ranking on hyperlinking performance, we emphasize the merits of rich visual information available in the anchors for the hyperlinking task, and the merits of ASR for large-scale search and hyperlinking.

This paper presents the MUST-VIS system for the MediaMixer/VideoLectures.NET Temporal Segmentation and Annotation Grand Challenge. The system allows users to visualize a lecture as a series of segments represented by keyword clouds, with relations to other similar lectures and segments. Segmentation is performed using a multi-factor algorithm which takes advantage of the audio (through automatic speech recognition and word-based segmentation) and video (through the detection of actions such as writing on the blackboard). The similarity across segments and lectures is computed using a content-based recommendation algorithm. Overall, the graph-based representation of segment similarity appears to be a promising and cost-effective approach to navigating lecture databases.

Despite the fact that performance improvements have been reported in the last years, semantic concept detection in video remains a challenging problem. Existing concept detection techniques, with ontology rules, exploit the static correlations among primitive concepts but not the dynamic spatiotemporal correlations. The proposed method rewards (or punishes) detected primitive concepts using dynamic spatiotemporal correlations of the given ontology rules and updates these ontology rules based on the accuracy of detection. Adaptively learned ontology rules significantly help in improving the overall accuracy of concept detection as shown in the experimental result.

Existing sequence pattern mining techniques assume that the obtained events from event detectors are accurate. However, in reality, event detectors label the events from different modalities with a certain probability over a time-interval. In this article, we consider for the first time Probabilistic Temporal Multimedia (PTM) Event data to discover accurate sequence patterns. PTM event data considers the start time, end time, event label and associated probability for the sequence pattern discovery. As the existing sequence pattern mining techniques cannot work on such realistic data, we have developed a novel framework for performing sequence pattern mining on probabilistic temporal multimedia event data. We perform probability fusion to resolve the redundancy among detected events from different modalities, considering their cross-modal correlation. We propose a novel sequence pattern mining algorithm called Probabilistic Interval based Event Miner (PIE-Miner) for discovering frequent sequence patterns from interval based events. PIE-Miner has a new support counting mechanism developed for PTM data. Existing sequence pattern mining algorithms have event label level support counting mechanism, whereas we have developed event cluster level support counting mechanism. We discover the complete set of all possible temporal relationships based on Allen's interval algebra. The experimental results showed that the discovered sequence patterns are more useful than the patterns discovered with state-of-the-art sequence pattern mining algorithms.