DCU linking runs at MediaEval 2013: search and hyperlinking task

We describe Dublin City University (DCU)'s participation in the Hyperlinking sub-task of the Search and Hyperlinking of Television Content task at MediaEval 2013. Two methods of video hyperlinking construction are reported: i) using spoken data annotation results to achieve the ranked hyperlink list, ii) linking and merging meaningful named entities in video segments to create hyperlinks. The details of algorithm design and evaluation are presented

The search and hyperlinking task at MediaEval 2013

The Search and Hyperlinking Task formed part of the MediaEval 2013 evaluation workshop. The Task consisted of two sub-tasks: (1) answering known-item queries from a collection of roughly 1200 hours of broadcast TV material, and (2) linking anchors within the known item to other parts of the video collection. We provide an overview of the task and the data sets used

Time-based segmentation and use of jump-in points in DCU search runs at the search and hyperlinking task at MediaEval 2013

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2013. Our experiments investigate the aect of using information about speech segment boundaries and pauses on the effectiveness of retrieving jump-in points within the retrieved segments. We segment all three available types of transcripts (automatic ones provided by LIMSI/Vocapia and LIUM, and manual subtitles provided by BBC) into fixed-length time units, and present the resulting runs using the original segment starts and using the potential jump-in points. Our method for adjustment of the jump-in points achieves higher scores for all LIMSI/Vocapia, LIUM, and subtitles based runs

Adapting Binary Information Retrieval Evaluation Metrics for Segment-based Retrieval Tasks

This report describes metrics for the evaluation of the effectiveness of segment-based retrieval based on existing binary information retrieval metrics. This metrics are described in the context of a task for the hyperlinking of video segments. This evaluation approach re-uses existing evaluation measures from the standard Cranfield evaluation paradigm. Our adaptation approach can in principle be used with any kind of effectiveness measure that uses binary relevance, and for other segment-baed retrieval tasks. In our video hyperlinking setting, we use precision at a cut-off rank n and mean average precision.Comment: Explanation of evaluation measures for the linking task of the MediaEval Workshop 201

Hierarchical Topic Models for Language-based Video Hyperlinking

International audienceWe investigate video hyperlinking based on speech transcripts , leveraging a hierarchical topical structure to address two essential aspects of hyperlinking, namely, serendipity control and link justification. We propose and compare different approaches exploiting a hierarchy of topic models as an intermediate representation to compare the transcripts of video segments. These hierarchical representations offer a basis to characterize the hyperlinks, thanks to the knowledge of the topics who contributed to the creation of the links, and to control serendipity by choosing to give more weights to either general or specific topics. Experiments are performed on BBC videos from the Search and Hyperlinking task at MediaEval. Link precisions similar to those of direct text comparison are achieved however exhibiting different targets along with a potential control of serendipity

Investigating domain-independent NLP techniques for precise target selection in video hyperlinking

International audienceAutomatic generation of hyperlinks in multimedia video data is a subject with growing interest, as demonstrated by recent work undergone in the framework of the Search and Hyperlinking task within the Mediaeval benchmark initiative. In this paper, we compare NLP-based strategies for precise target selection in video hyperlinking exploiting speech material, with the goal of providing hyperlinks from a specified anchor to help information retrieval. We experimentally compare two approaches enabling to select short portions of videos which are relevant and possibly complementary with respect to the anchor. The first approach exploits a bipartite graph relating utterances and words to find the most relevant utterances. The second one uses explicit topic segmentation, whether hierarchical or not, to select the target segments. Experimental results are reported on the Mediaeval 2013 Search and Hyperlinking dataset which consists of BBC videos, demonstrating the interest of hierarchical topic segmentation for precise target selection

Multimedia information seeking through search and hyperlinking

Searching for relevant webpages and following hyperlinks to related content is a widely accepted and effective approach to information seeking on the textual web. Existing work on multimedia information retrieval has focused on search for individual relevant items or on content linking without specific attention to search results. We describe our research exploring integrated multimodal search and hyperlinking for multimedia data. Our investigation is based on the MediaEval 2012 Search and Hyperlinking task. This includes a known-item search task using the Blip10000 internet video collection, where automatically created hyperlinks link each relevant item to related items within the collection. The search test queries and link assessment for this task was generated using the Amazon Mechanical Turk crowdsourcing platform. Our investigation examines a range of alternative methods which seek to address the challenges of search and hyperlinking using multimodal approaches. The results of our experiments are used to propose a research agenda for developing eective techniques for search and hyperlinking of multimedia content

The search and hyperlinking task at MediaEval 2014

The Search and Hyperlinking Task at MediaEval 2014 is the third edition of this task. As in previous versions, it consisted of two sub-tasks: (i) answering search queries from a collection of roughly 2700 hours of BBC broadcast TV material, and (ii) linking anchor segments from within the videos to other target segments within the video collection. For MediaEval 2014, both sub-tasks were based on an ad-hoc retrieval scenario, and were evaluated using a pooling procedure across participants submissions with crowdsourcing relevance assessment using Amazon Mechanical Turk

UPC at MediaEval 2013 Hyperlinking Task

These working notes paper present the contribution of the UPC team to the Hyperlinking sub-task of the Search and Hyperlinking Task in MediaEval 2013. Our contribution ex- plores the potential of a solution based only on visual cues. In particular, every automatically generated shot is repre- sented by a keyframe. The linking between video segments is based on the visual similarity of the keyframes they contain. Visual similarity is assessed with the intersection of bag of features histograms generated with the SURF descriptor.Postprint (published version

SAVA at MediaEval 2015: search and anchoring in video archives

The Search and Anchoring in Video Archives (SAVA) task at MediaEval 2015 consists of two sub-tasks: (i) search for multimedia content within a video archive using multimodal queries referring to information contained in the audio and visual streams/content, and (ii) automatic selection of video segments within a list of videos that can be used as anchors for further hyperlinking within the archive. The task used a collection of roughly 2700 hours of the BBC broadcast TV material for the former sub-task, and about 70 les taken from this collection for the latter sub-task. The search sub-task is based on an ad-hoc retrieval scenario, and is evaluated using a pooling procedure across participants submissions with crowdsourcing relevance assessment using Amazon Mechanical Turk (MTurk). The evaluation used metrics that are variations of MAP adjusted for this task. For the anchor selection sub-task overlapping regions of interest across participants submissions were assessed using MTurk workers, and mean reciprocal rank (MRR), precision and recall were calculated for evaluation