short-paper

Vlfeat: an open and portable library of computer vision algorithms

Authors:

Andrea Vedaldi,

Brian FulkersonAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 1469 - 1472

https://doi.org/10.1145/1873951.1874249

Published: 25 October 2010 Publication History

Abstract

VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.

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A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.

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Cited By

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https://doi.org/10.3934/ipi.2024022
Li HWang LZhao TZhao W(2024)Local-Peak Scale-Invariant Feature Transform for Fast and Random Image StitchingSensors10.3390/s2417575924:17(5759)Online publication date: 4-Sep-2024
https://doi.org/10.3390/s24175759
Soltanian MBorna K(2024)Video Classification Using Smooth Approximation of Hard-assignment EncodingJournal of Information Processing10.2197/ipsjjip.32.64132(641-651)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.641
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Index Terms

Vlfeat: an open and portable library of computer vision algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding
2. Software and its engineering

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 October 2010

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MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Li YYang YYin KDuan YYuan J(2024)The truncated variational model for image labeling and graph partitioningInverse Problems and Imaging10.3934/ipi.2024022(0-0)Online publication date: 2024
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Li HWang LZhao TZhao W(2024)Local-Peak Scale-Invariant Feature Transform for Fast and Random Image StitchingSensors10.3390/s2417575924:17(5759)Online publication date: 4-Sep-2024
https://doi.org/10.3390/s24175759
Soltanian MBorna K(2024)Video Classification Using Smooth Approximation of Hard-assignment EncodingJournal of Information Processing10.2197/ipsjjip.32.64132(641-651)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.641
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Huang JLi HGong YFan FMa YDu QMa J(2024)Robust Feature Matching via Graph Neighborhood Motion ConsensusIEEE Transactions on Multimedia10.1109/TMM.2024.339826626(9790-9803)Online publication date: 8-May-2024
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