Graphing of E-Science Data with varying user requirements

Based on our experience in the Swiss Experiment, exploring experimental, scientific data is often done in a visual way. Starting from a global overview the users are zooming in on interesting events. In case of huge data volumes special data structures have to be introduced to provide fast and easy access to the data. Since it is hard to predict on how users will work with the data a generic approach requires self-adaptation of the required special data structures. In this paper we describe the underlying NP-hard problem and present several approaches to address the problem with varying properties. The approaches are illustrated with a small example and are evaluated with a synthetic data set and user queries

A Simulator for Concept Detector Output

Concept based video retrieval is a promising search paradigm because it is fully automated and it investigates the fine grained content of a video, which is normally not captured by human annotations. Concepts are captured by so-called concept detectors. However, since these detectors do not yet show a sufficient performance, the evaluation of retrieval systems, which are built on top of the detector output, is difficult. In this report we describe a software package which generates simulated detector output for a specified performance level. Afterwards, this output can be used to execute a search run and ultimately to evaluate the performance of the proposed retrieval method, which is normally done through comparison to a baseline. The probabilistic model of the detectors are two Gaussians, one for the positive and one for the negative class. Thus, the parameters for the simulation are the two means and deviations plus the prior probability of the concept in the dataset

University of Twente at the TREC 2008 Enterprise Track: 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.\ud This is the fourth (and the last) year of TREC 2007 Enterprise Track and the second year the University of Twente (Database group) submitted runs for the expert nding task. In the methods that were used to produce these runs, we mostly rely on the predicting potential of those expertise evidence sources that are publicly available on the Global Web, but not hosted at the website of the organization under study (CSIRO). This paper describes the follow-up studies\ud complimentary to our recent research [8] that demonstrated how taking the web factor seriously signicantly improves the performance of expert nding in the enterprise

The Effectiveness of Concept Based Search for Video Retrieval

In this paper we investigate how a small number of high-level concepts\ud derived for video shots, such as Sport. Face.Indoor. etc., can be used effectively for ad hoc search in video material. We will answer the following questions: 1) Can we automatically construct concept queries from ordinary text queries? 2) What is the best way to combine evidence from single concept detectors into final search results? We evaluated algorithms for automatic concept query formulation using WordNet based concept extraction, and we evaluated algorithms for fast, on-line combination of concepts. Experimental results on data from the TREC Video 2005 workshop and 25 test users show the following. 1) Automatic query formulation through WordNet based concept extraction can achieve comparable results to user created query concepts and 2) Combination methods that take neighboring shots into account outperform more simple combination methods

UTwente does Rich Speech Retrieval at MediaEval 2011

This paper describes the participation of the University of Twente team at the Rich Text Retrieval Task of the Media Eval Benchmark Initiative 2011. The goal of the task is to find entry points of relevant parts of videos to reduce the browsing effort of searchers. This is our first participation, therefore our main focus is to create a baseline system which can be improved in the future. We experiment with different evidence sources (ASR and meta data) together with a basic score combination function. We also experiment with different entry points relative to the segments found by the contained evidence

The Lowlands team at TRECVID 2008

In this paper we describe our experiments performed for TRECVID 2008. We participated in the High Level Feature extraction and the Search task. For the High Level Feature extraction task we mainly installed our detection environment. In the Search task we applied our new PRFUBE ranking model together with an estimation method which estimates a vital parameter of the model, the probability of a concept occurring in relevant shots. The PRFUBE model has similarities to the well known Probabilistic Text Information Retrieval methodology and follows the Probability Ranking Principle

Two selfless contributions to web search evaluation

We present our results for the Web Search track and the Federated Web Search track for the 23rd Text Retrieval Conference TREC

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

The AXES-lite video search engine

The aim of AXES is to develop tools that provide various types of users with new engaging ways to interact with audiovisual libraries, helping them discover, browse, navigate, search, and enrich archives. This paper describes the initial (lite) version of the AXES search engine, which is targeted at professional users such as media professionals and archivists. We describe the overall system design, the user interface, and the results of our experiments at TRECVid 2011

The uncertain representation ranking framework for concept-based video retrieval

Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance