UTwente does Brave New Tasks for MediaEval 2012: Searching and Hyperlinking

In this paper we report our experiments and results for the brave new searching and hyperlinking tasks for the MediaEval Benchmark Initiative 2012. The searching task involves nding target video segments based on a short natural language sentence query and the hyperlinking task involves nding links from the target video segments to other related video segments in the collection using a set of anchor segments in the videos that correspond to the textual search queries. To nd the starting points in the video, we only used speech transcripts and metadata as evidence source, however, other visual features (for e.g., faces, shots and keyframes) might also aect results for a query. We indexed speech transcripts and metadata, furthermore, the speech transcripts were indexed at speech segment level and at sentence level to improve the likelihood of nding jump-in-points. For linking video segments, we computed k-nearest neighbours of video segments using euclidean distance

Using the Global Web as an Expertise Evidence Source

This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track. The presented study demonstrates the predicting potential of the expertise evidence that can be found outside of the organization. We discovered that combining the ranking built solely on the Enterprise data with the Global Web based ranking may produce significant increases in performance. However, our main goal was to explore whether this result can be further improved by using various quality measures to distinguish among web result items. While, indeed, it was beneficial to use some of these measures, especially those measuring relevance of URL strings and titles, it stayed unclear whether they are decisively important

Simulating the Future of Concept-Based Video Retrieval under Improved Detector Performance

In this paper we address the following important questions for concept-based video retrieval: (1) What is the impact of detector performance on the performance of concept-based retrieval engines, and (2) will these engines be applicable to real-life search tasks if detector performance improves in the future? We use Monte Carlo simulations to answer these questions. To generate the simulation input, we propose to use a probabilistic model of two Gaussians for the confidence scores that concept detectors emit. Modifying the model's parameters affects the detector performance and the search performance. We study the relation between these two performances on two video collections. For detectors with similar discriminative power and a concept vocabulary of around 100 concepts, the simulation reveals that in order to achieve a search performance of 0.20 mean average precision (MAP) -- which is considered sufficient performance for real-life applications -- one needs detectors with at least 0.60 MAP. We also find that, given our simulation model and low detector performance, MAP is not always a good evaluation measure for concept detectors since it is not strongly correlated with the search performance

Linking inside a video collection - what and how to measure?

Although linking video to additional information sources seems to be a sensible approach to satisfy information needs of user, the perspective of users is not yet analyzed on a fundamental level in real-life scenarios. However, a better understanding of the motivation of users to follow links in video, which anchors users prefer to link from within a video, and what type of link targets users are typically interested in, is important to be able to model automatic linking of audiovisual content appropriately. In this paper we report on our methodology towards eliciting user requirements with respect to video linking in the course of a broader study on user requirements in searching and a series of benchmark evaluations on searching and linking

AXES at TRECVid 2011

Abstract The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments

Rocchio-based relevance feedback in video event retrieval

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