Dublin City University at the TREC 2005 terabyte track

For the 2005 Terabyte track in TREC Dublin City University participated in all three tasks: Adhoc, E±ciency and Named Page Finding. Our runs for TREC in all tasks were primarily focussed on the application of "Top Subset Retrieval" to the Terabyte Track. This retrieval utilises different types of sorted inverted indices so that less documents are processed in order to reduce query times, and is done so in a way that minimises loss of effectiveness in terms of query precision. We also compare a distributed version of our Físréal search system [1][2] against the same system deployed on a single machine

Dublin City University at the TREC 2006 terabyte track

For the 2006 Terabyte track in TREC, Dublin City University’s participation was focussed on the ad hoc search task. As per the pervious two years [7, 4], our experiments on the Terabyte track have concentrated on the evaluation of a sorted inverted index, the aim of which is to sort the postings within each posting list in such a way, that allows only a limited number of postings to be processed from each list, while at the same time minimising the loss of effectiveness in terms of query precision. This is done using the Físréal search system, developed at Dublin City University [4, 8]

A study of selection noise in collaborative web search

Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We consider this issue by analysing the results of collaborative Web search in circumstances where the behaviour of searchers is unreliable

Structural term extraction for expansion of template-based genomic queries

This paper describes our experiments run to address the ad hoc task of the TREC 2005 Genomics track. The task topics were expressed with 5 different structures called Generic Topic Templates (GTTs). We hypothesized the presence of GTT-specific structural terms in the free-text fields of documents relevant to a topic instantiated from that same GTT. Our experiments aimed at extracting and selecting candidate structural terms for each GTT. Selected terms were used to expand initial queries and the quality of the term selection was measured by the impact of the expansion on initial search results. The evaluation used the task training topics and the associated relevance information. This paper describes the two term extraction methods used in the experiments and the resulting two runs sent to NIST for evaluation

Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced

TRECVid 2005 experiments at Dublin City University

In this paper we describe our experiments in the automatic and interactive search tasks and the BBC rushes pilot task of TRECVid 2005. Our approach this year is somewhat different than previous submissions in that we have implemented a multi-user search system using a DiamondTouch tabletop device from Mitsubishi Electric Research Labs (MERL).We developed two versions of oursystem one with emphasis on efficient completion of the search task (Físchlár-DT Efficiency) and the other with more emphasis on increasing awareness among searchers (Físchlár-DT Awareness). We supplemented these runs with a further two runs one for each of the two systems, in which we augmented the initial results with results from an automatic run. In addition to these interactive submissions we also submitted three fully automatic runs. We also took part in the BBC rushes pilot task where we indexed the video by semi-automatic segmentation of objects appearing in the video and our search/browsing system allows full keyframe and/or object-based searching. In the interactive search experiments we found that the awareness system outperformed the efficiency system. We also found that supplementing the interactive results with results of an automatic run improves both the Mean Average Precision and Recall values for both system variants. Our results suggest that providing awareness cues in a collaborative search setting improves retrieval performance. We also learned that multi-user searching is a viable alternative to the traditional single searcher paradigm, provided the system is designed to effectively support collaboration

Físchlár-DiamondTouch: collaborative video searching on a table

In this paper we present the system we have developed for our participation in the annual TRECVid benchmarking activity, specically the system we have developed, Físchlár-DT, for participation in the interactive search task of TRECVid 2005. Our back-end search engine uses a combination of a text search which operates over the automatic speech recognised text, and an image search which uses low-level image features matched against video keyframes. The two novel aspects of our work are the fact that we are evaluating collaborative, team-based search among groups of users working together, and that we are using a novel touch-sensitive tabletop interface and interaction device known as the DiamondTouch to support this collaborative search. The paper summarises the backend search systems as well as presenting the interface we have developed, in detail

TRECVID 2004 experiments in Dublin City University

In this paper, we describe our experiments for TRECVID 2004 for the Search task. In the interactive search task, we developed two versions of a video search/browse system based on the Físchlár Digital Video System: one with text- and image-based searching (System A); the other with only image (System B). These two systems produced eight interactive runs. In addition we submitted ten fully automatic supplemental runs and two manual runs. A.1, Submitted Runs: • DCUTREC13a_{1,3,5,7} for System A, four interactive runs based on text and image evidence. • DCUTREC13b_{2,4,6,8} for System B, also four interactive runs based on image evidence alone. • DCUTV2004_9, a manual run based on filtering faces from an underlying text search engine for certain queries. • DCUTV2004_10, a manual run based on manually generated queries processed automatically. • DCU_AUTOLM{1,2,3,4,5,6,7}, seven fully automatic runs based on language models operating over ASR text transcripts and visual features. • DCUauto_{01,02,03}, three fully automatic runs based on exploring the benefits of multiple sources of text evidence and automatic query expansion. A.2, In the interactive experiment it was confirmed that text and image based retrieval outperforms an image-only system. In the fully automatic runs, DCUauto_{01,02,03}, it was found that integrating ASR, CC and OCR text into the text ranking outperforms using ASR text alone. Furthermore, applying automatic query expansion to the initial results of ASR, CC, OCR text further increases performance (MAP), though not at high rank positions. For the language model-based fully automatic runs, DCU_AUTOLM{1,2,3,4,5,6,7}, we found that interpolated language models perform marginally better than other tested language models and that combining image and textual (ASR) evidence was found to marginally increase performance (MAP) over textual models alone. For our two manual runs we found that employing a face filter disimproved MAP when compared to employing textual evidence alone and that manually generated textual queries improved MAP over fully automatic runs, though the improvement was marginal. A.3, Our conclusions from our fully automatic text based runs suggest that integrating ASR, CC and OCR text into the retrieval mechanism boost retrieval performance over ASR alone. In addition, a text-only Language Modelling approach such as DCU_AUTOLM1 will outperform our best conventional text search system. From our interactive runs we conclude that textual evidence is an important lever for locating relevant content quickly, but that image evidence, if used by experienced users can aid retrieval performance. A.4, We learned that incorporating multiple text sources improves over ASR alone and that an LM approach which integrates shot text, neighbouring shots and entire video contents provides even better retrieval performance. These findings will influence how we integrate textual evidence into future Video IR systems. It was also found that a system based on image evidence alone can perform reasonably and given good query images can aid retrieval performance

Interactive searching and browsing of video archives: using text and using image matching

Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace, ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing. One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content

Index ordering by query-independent measures

There is an ever-increasing amount of data that is being produced from various data sources — this data must then be organised effectively if we hope to search though it. Traditional information retrieval approaches search through all available data in a particular collection in order to find the most suitable results, however, for particularly large collections this may be extremely time consuming. Our purposed solution to this problem is to only search a limited amount of the collection at query-time, in order to speed this retrieval process up. Although, in doing this we aim to limit the loss in retrieval efficacy (in terms of accuracy of results). The way we aim to do this is to firstly identify the most “important” documents within the collection, and then sort the documents within the collection in order of their "importance” in the collection. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which should not only make the search more efficient, but should also limit any loss in retrieval accuracy. In this thesis we investigate various different query-independent methods that may indicate the importance of a document in a collection. The more accurate the measure is at determining an important document, the more effectively we can eliminate documents from the retrieval process - improving the query-throughput of the system, as well as providing a high level of accuracy in the returned results. The effectiveness of these approaches are evaluated using the datasets provided by the terabyte track at the Text REtreival Conference (TREC)