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.

References

[1]

A. Bosch, A. Zisserman, and X. Muñz. Image classification using random forests and ferns. In Proc. ICCV, 2007.

[2]

C. Elkan. Using the triangle inequality to accelerate k-means. In Proc. ICML, 2003.

[3]

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 2008.

Digital Library

[4]

L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proc. ICCV, 2003.

Digital Library

[5]

P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In Proc. ICCV, 2009.

[6]

S. Lazebnik, C. Schmid, and J. Ponce. Beyond bag of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006.

Digital Library

[7]

S. Lloyd. Least square quantization in PCM. IEEE Trans. on Information Theory, 28(2), 1982.

[8]

D. Lowe. Implementation of the scale invariant feature transform. http://www.cs.ubc.ca/~lowe/keypoints/, 2007.

[9]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2(60):91--110, 2004.

Digital Library

[10]

J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In Proc. BMVC, 2002.

[11]

M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithmic configuration. In Proc. VISAPP, 2009.

[12]

M. Rousson. A VLFeat Python wrapper. http://github.com/mmmikael/vlfeat, 2009.

[13]

J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV, 2003.

Digital Library

[14]

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

[15]

A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In Proc. ECCV, 2008.

[16]

A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In Proc. CVPR, 2010.

Cited By

Nabli HGhannem ADjemaa RSliman L(2025)How innovative technologies shape the future of pharmaceutical supply chainsComputers & Industrial Engineering10.1016/j.cie.2024.110745199(110745)Online publication date: Jan-2025
https://doi.org/10.1016/j.cie.2024.110745
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
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
Show More Cited By

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

Recommendations

Animal classification using facial images with score‐level fusion

A real‐world animal biometric system that detects and describes animal life in image and video data is an emerging subject in machine vision. These systems develop computer vision approaches for the classification of animals. A novel method for animal ...
Speed Estimation Using Computer Vision (Abstract Only)
SIGCSE '15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education

With recent advancements in smartphone digital camera technology, the opportunity to integrate computer vision into daily activities has become possible. A unique application of computer vision is ESPN's use of K-Zone during Major League Baseball ...
Machine Learning in Computer Vision
Abstract
During last few years the computer applications have gone dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volume of data collected through sensors and internet. The idea ...

Comments

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MM '10

Sponsor:

SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,008
Total Citations
View Citations
3,611
Total Downloads

Downloads (Last 12 months)94
Downloads (Last 6 weeks)8

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nabli HGhannem ADjemaa RSliman L(2025)How innovative technologies shape the future of pharmaceutical supply chainsComputers & Industrial Engineering10.1016/j.cie.2024.110745199(110745)Online publication date: Jan-2025
https://doi.org/10.1016/j.cie.2024.110745
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
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
Almeida RCarvalho JVieira Ade Oliveira HD’Angelo M(2024)Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural NetworkApplied Sciences10.3390/app1501014715:1(147)Online publication date: 27-Dec-2024
https://doi.org/10.3390/app15010147
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
Zou YPei SSun ZZhao QLiu HLiu YYang R(2024)A 3D Reconstruction Method for Heterogeneous Data of Power Equipment Based on Improved Neural Radiation FieldsIEEE Transactions on Power Delivery10.1109/TPWRD.2024.342191339:5(2677-2692)Online publication date: Oct-2024
https://doi.org/10.1109/TPWRD.2024.3421913
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
https://dl.acm.org/doi/10.1109/TMM.2024.3398266
Zhang TLu MHu YHao QWu JZhang NYang SHe WZhang FTao R(2024)End-to-End Direct Phase Retrieval From a Single-Frame Interferogram Based on Deep LearningIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.341811273(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3418112
Shen LZhang YChen CWang LZhu JHe Y(2024)Extended Neighborhood Consensus With Affine Correspondence for Outlier Filtering in Feature MatchingIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.338858062(1-15)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3388580
Wang JDu PYang SZhang ZNing J(2024)A Spatial Arrangement Preservation-Based Stitching Method via Geographic Coordinates of UAV for Farmland Remote Sensing ImageIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.337407562(1-13)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3374075
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents