Results 11 to 20 of about 585,419 (256)

Query-by-example [PDF]

open access: bronzeProceedings of the May 19-22, 1975, national computer conference and exposition on - AFIPS '75, 1975
In the last few years we have witnessed a trend to appeal to the non-professional user who has little or virtually no computer or mathematical background.
Moshé M. Zloof
  +6 more sources

Fuzzy Query By Example

open access: greenProceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018
This paper describes Fuzzy Query By Example, an approach helping users retrieve data without any prior knowledge of the database schema or any formal querying language. The user is solicited to evaluate, in a binary way, pre-selected items of the database.
Aurélien Moreau   +2 more
  +8 more sources

Query-by-example [PDF]

open access: bronzeProceedings of the June 7-10, 1976, national computer conference and exposition on - AFIPS '76, 1976
Query-by-Example is a high level non-procedural data base language which provides the end user with a simplified unified interface for querying, updating, defining, and maintaining, the data base, as well as imbedding various integrity and authority constraints.
Moshé M. Zloof
openalex   +4 more sources

A QUERY BY EXAMPLE MUSIC RETRIEVAL ALGORITHM [PDF]

open access: greenDigital Media Processing for Multimedia Interactive Services, 2003
This paper deals with the problem of Query by Example Music Retrieval (QEMR). Retrieving music pieces that are "similar" to a musical query is crucial when exploring very big music databases. The term "similarity" in this paper is equivalent, for instance, to the rules permitting a human subject to build a list of songs to listen to. While the Query by
Hadi Harb, L. CHEN
openalex   +4 more sources

A study of query by semantic example [PDF]

open access: green2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
In recent years, query-by-semantic-example (QBSE) has become a popular approach to do content based image retrieval. QBSE extends the well established query-by-example retrieval paradigm to the semantic domain. While various authors have pointed out the benefits of QBSE, there are still various open questions with respect to this paradigm.
Nikhil Rasiwasia, Nuno Vasconcelos
openalex   +3 more sources

Attention-based audio embeddings for query-by-example [PDF]

open access: yes, 2022
An ideal audio retrieval system efficiently and robustly recognizes a short query snippet from an extensive database. However, the performance of well-known audio fingerprinting systems falls short at high signal distortion levels. This paper presents an audio retrieval system that generates noise and reverberation robust audio fingerprints using the ...
Arora, V., Demuynck, Kris, Singh, Anup
core   +4 more sources

An Introduction to the Patstat Database with Example Queries [PDF]

open access: yesAustralian Economic Review, 2014
AbstractThis article provides an introduction to the Patstat patent database. It offers guided examples of 10 popular queries that are relevant for research purposes and that cover the most important data tables. It is targeted at academic researchers and practitioners who are willing to learn the basics of the database.
Gaétan de Rassenfosse   +2 more
openaire   +6 more sources

Query-by-Example On-Device Keyword Spotting [PDF]

open access: yes2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing.
Yeonseok Kim   +4 more
openaire   +2 more sources

Bridging the Gap: Query by Semantic Example [PDF]

open access: yesIEEE Transactions on Multimedia, 2007
A combination of query-by-visual-example (QBVE) and semantic retrieval (SR), denoted as query-by-semantic-example (QBSE), is proposed. Images are labeled with respect to a vocabulary of visual concepts, as is usual in SR. Each image is then represented by a vector, referred to as a semantic multinomial, of posterior concept probabilities.
Rasiwasia, Nikhil   +2 more
openaire   +3 more sources

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