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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Journal ArticleDOI
01 Aug 2008
TL;DR: This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram, which exploits the current knowledge of the query point and the search space in addition to the data objects.
Abstract: The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*-Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*-Diagram outperforms the best existing technique by two orders of magnitude.

110 citations

Proceedings ArticleDOI
26 Oct 2008
TL;DR: A novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora and promising results show that the proposed method is more effective than other state-of-the-art approaches for near- DUplicateKeyframe retrieval.
Abstract: Near-duplicate image retrieval plays an important role in many real-world multimedia applications. Most previous approaches have some limitations. For example, conventional appearance-based methods may suffer from the illumination variations and occlusion issue, and local feature correspondence-based methods often do not consider local deformations and the spatial coherence between two point sets. In this paper, we propose a novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora. In contrast to previous approaches, the NIM technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and find out the correct correspondences from noisy data effectively. To make our technique applicable to large-scale applications, we suggest an effective multi-level ranking scheme that filters out the irrelevant results in a coarse-to-fine manner. In our ranking scheme, to overcome the extremely small training size challenge, we employ a semi-supervised learning method for improving the performance using unlabeled data. To evaluate the effectiveness of our solution, we have conducted extensive experiments on two benchmark testbeds extracted from the TRECVID2003 and TRECVID2004 corpora. The promising results show that our proposed method is more effective than other state-of-the-art approaches for near-duplicate keyframe retrieval.

109 citations

Journal ArticleDOI
Kui Lam Kwok1
TL;DR: How probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network is shown and performance of feedback improves substantially over no feedback, and further gains are obtained when queries are expanded with terms from the feedback documents.
Abstract: In this article we show how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with four standard small collections and a large Wall Street Journal collection (173,219 documents) show that performance of feedback improves substantially over no feedback, and further gains are obtained when queries are expanded with terms from the feedback documents. The effect is much more pronounced in small collections than in the large collection. Query expansion may be considered as a tool for both precision and recall enhancement. In particular, small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve.

109 citations

Proceedings ArticleDOI
04 Nov 2005
TL;DR: This paper addresses document indexing and retrieval using geographical location by surveying known approaches and showing how they can be combined to build an effective Geo-IR system.
Abstract: This paper addresses document indexing and retrieval using geographical location. It discusses possible indexing structures and result ranking algorithms, surveying known approaches and showing how they can be combined to build an effective Geo-IR system.

109 citations

25 Jun 1997
TL;DR: An internet search engine that helps the user formulate their query by a process of navigation through a structured, automatically constructed, information space called a hyperindex, which aids the user in query term addition and deletion is described.
Abstract: Often queries to internet search engines consist of one or two terms. As a consequence, the effectiveness of the retrieval suffers. This paper describes an internet search engine that helps the user formulate their query by a process of navigation through a structured, automatically constructed, information space called a hyperindex. In the first part of this paper, the logs of an internet search engine were analyzed to determine the proportions with which different types of query transformation occur. It was found that the primary transformation type was repetition of the previous query. Users also substitute, add and delete terms from a previous query and with lower frequency split compound terms, make changes to spelling, punctuation, and case and use derivative forms of words and abbreviations. The second part of the paper details the hyperindex - which aids the user in query term addition and deletion. The architecture of a hyperindex-based internet search engine is presented. Some initial practical experiences are also discussed.

109 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168