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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
TL;DR: Thorough experiments suggest that the proposed saliency- inspired fast image retrieval scheme, S-sim, significantly speeds up online retrieval and outperforms the state-of-the-art BoW-based image retrieval schemes.
Abstract: The bag-of-visual-words (BoW) model is effective for representing images and videos in many computer vision problems, and achieves promising performance in image retrieval. Nevertheless, the level of retrieval efficiency in a large-scale database is not acceptable for practical usage. Considering that the relevant images in the database of a given query are more likely to be distinctive than ambiguous, this paper defines “database saliency” as the distinctiveness score calculated for every image to measure its overall “saliency” in the database. By taking advantage of database saliency, we propose a saliency- inspired fast image retrieval scheme, S-sim, which significantly improves efficiency while retains state-of-the-art accuracy in image retrieval . There are two stages in S-sim: the bottom-up saliency mechanism computes the database saliency value of each image by hierarchically decomposing a posterior probability into local patches and visual words, the concurrent information of visual words is then bottom-up propagated to estimate the distinctiveness, and the top-down saliency mechanism discriminatively expands the query via a very low-dimensional linear SVM trained on the top-ranked images after initial search, ranking images are then sorted on their distances to the decision boundary as well as the database saliency values. We comprehensively evaluate S-sim on common retrieval benchmarks, e.g., Oxford and Paris datasets. Thorough experiments suggest that, because of the offline database saliency computation and online low-dimensional SVM, our approach significantly speeds up online retrieval and outperforms the state-of-the-art BoW-based image retrieval schemes.

101 citations

Proceedings ArticleDOI
29 Jun 2009
TL;DR: This paper proposes to take as input a target database and then generate and index a set of query forms offline, to address challenges that arise in form generation, keyword search over forms, and ranking and displaying these forms.
Abstract: A common criticism of database systems is that they are hard to query for users uncomfortable with a formal query language. To address this problem, form-based interfaces and keyword search have been proposed; while both have benefits, both also have limitations. In this paper, we investigate combining the two with the hopes of creating an approach that provides the best of both. Specifically, we propose to take as input a target database and then generate and index a set of query forms offline. At query time, a user with a question to be answered issues standard keyword search queries; but instead of returning tuples, the system returns forms relevant to the question. The user may then build a structured query with one of these forms and submit it back to the system for evaluation. In this paper, we address challenges that arise in form generation, keyword search over forms, and ranking and displaying these forms. We explore techniques to tackle these challenges, and present experimental results suggesting that the approach of combining keyword search and form-based interfaces is promising.

101 citations

Patent
05 Jun 2001
TL;DR: The Network of Qualified Knowledge (NQK) as discussed by the authors is a solution to the problems of current search techniques on the Internet (volume, ranking, difficulty to assess) and extends the solution to all kinds of electronic information accessible through networks and databases.
Abstract: This invention addresses the problems of current search techniques on the Internet—volume, ranking, difficulty to assess—and extends the solution to all kinds of electronic information accessible through networks and databases. The solution principle engages the help of specialists in particular domains and supplies them with tools to effectively scour the information resources for high quality information in their field, to commit that knowledge to distributed databases, to construct dedicated knowledge environments, and to submit corresponding context information to centralized registries. End users implicitly access mirrored services of these registries and use the context information to focus their searches onto the resources qualified by the expert network. Many of the individual techniques involved in building the tools for deployment, operation and exploitation of such “Networks of Qualified Knowledge” are well known and may in the future be replaced by more effective techniques. The essence of the invention lies in the way these techniques are put to use to implement the presented process.

101 citations

Proceedings ArticleDOI
25 Jun 2005
TL;DR: A novel entity-based representation of discourse is presented which is inspired by Centering Theory and can be computed automatically from raw text and achieves significantly higher accuracy than a state-of-the-art coherence model.
Abstract: This paper considers the problem of automatic assessment of local coherence. We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text. We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model.

101 citations

Posted Content
Nikolay Savinov1, Akihito Seki2, Lubor Ladicky1, Torsten Sattler1, Marc Pollefeys1 
TL;DR: This paper is the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner, and shows that this unsupervised method performs better or on-par with baselines on two tasks.
Abstract: Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer vision, many hand-crafted solutions were proposed. In this paper, we ask a fundamental question: can we learn such detectors from scratch? Since it is often unclear what points are "interesting", human labelling cannot be used to find a truly unbiased solution. Therefore, the task requires an unsupervised formulation. We are the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner. Interest points are then extracted from the top/bottom quantiles of this ranking. We validate our approach on two tasks: standard RGB image interest point detection and challenging cross-modal interest point detection between RGB and depth images. We quantitatively show that our unsupervised method performs better or on-par with baselines.

101 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