1,249 research outputs found
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
A new metric for patent retrieval evaluation
Patent retrieval is generally considered to be a recall-oriented information retrieval task that is growing in importance. Despite this fact, precision based scores such as mean average precision (MAP) remain the primary evaluation measures for patent retrieval. Our study examines different evaluation measures for the recall-oriented patent retrieval task and shows the limitations
of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and user search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the expected search effort of patent searchers
Evaluation campaigns and TRECVid
The TREC Video Retrieval Evaluation (TRECVid) is an
international benchmarking activity to encourage research
in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video
corpus, automatic detection of a variety of semantic and
low-level video features, shot boundary detection and the
detection of story boundaries in broadcast TV news. This
paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether
such campaigns are a good thing or a bad thing. There are
arguments for and against these campaigns and we present
some of them in the paper concluding that on balance they
have had a very positive impact on research progress
CLEF 2005: Ad Hoc track overview
We describe the objectives and organization of the CLEF 2005 ad hoc track and discuss the main characteristics of the tasks offered to test monolingual, bilingual and multilingual textual document retrieval. The performance achieved for each task is presented and a preliminary analysis of results is given. The paper focuses in particular on the multilingual tasks which reused the test collection created in CLEF 2003 in an attempt to see if an improvement in system performance over time could be measured, and also to examine the multilingual results merging problem
Что и как спрашивают в социальных вопросно-ответных сервисах по-русски?
In our study we surveyed different approaches to the study of questions in traditional linguistics, question answering (QA), and, recently, in community question answering (CQA). We adapted a functional-semantic classification scheme for CQA data and manually labeled 2,000 questions in Russian originating from [email protected] CQA service. About half of them are purely conversational and do not aim at obtaining actual information. In the subset of meaningful questions the major classes are requests for recommendations, or how-questions, and fact-seeking questions. The data demonstrate a variety of interrogative sentences as well as a host of formally non-interrogative expressions with the meaning of questions and requests. The observations can be of interest both for linguistics and for practical applications
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
External query reformulation for text-based image retrieval
In text-based image retrieval, the Incomplete Annotation
Problem (IAP) can greatly degrade retrieval effectiveness. A standard method used to address this problem is pseudo relevance feedback (PRF) which updates user queries by adding feedback terms selected automatically from top ranked documents in a prior retrieval run. PRF assumes that the target collection provides enough feedback information to select effective expansion terms. This is often not the case in image retrieval since images often only have short metadata annotations leading to the IAP. Our work proposes the use of an external knowledge resource (Wikipedia) in the process of refining user queries. In our method, Wikipedia documents strongly related to the terms in user query ("
definition documents") are first identified by title matching between the query and titles of Wikipedia articles. These definition documents are used as indicators to re-weight the feedback documents from an initial search
run on a Wikipedia abstract collection using the Jaccard coefficient. The new weights of the feedback documents are combined with the scores rated by different indicators. Query-expansion terms are then selected based on these new weights for the feedback documents. Our method is evaluated on the ImageCLEF WikipediaMM image retrieval task using text-based retrieval on the document metadata fields. The results show significant improvement compared to standard PRF methods
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