Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision
The introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2016-03-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.5772/62162 |
| _version_ | 1818240137108127744 |
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| author | Andreas Persson Amy Loutfi |
| author_facet | Andreas Persson Amy Loutfi |
| author_sort | Andreas Persson |
| collection | DOAJ |
| description | The introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum ) for improved feature matching of binary point-features. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications. |
| first_indexed | 2024-12-12T13:08:39Z |
| format | Article |
| id | doaj.art-bcebc56fc3c4422bb2b5af72b4538dff |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-12T13:08:39Z |
| publishDate | 2016-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-bcebc56fc3c4422bb2b5af72b4538dff2022-12-22T00:23:34ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-03-011310.5772/6216210.5772_62162Fast Matching of Binary Descriptors for Large-Scale Applications in Robot VisionAndreas Persson0Amy Loutfi1 University of Örebro, Örebro, Sweden University of Örebro, Örebro, SwedenThe introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum ) for improved feature matching of binary point-features. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.https://doi.org/10.5772/62162 |
| spellingShingle | Andreas Persson Amy Loutfi Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision International Journal of Advanced Robotic Systems |
| title | Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision |
| title_full | Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision |
| title_fullStr | Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision |
| title_full_unstemmed | Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision |
| title_short | Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision |
| title_sort | fast matching of binary descriptors for large scale applications in robot vision |
| url | https://doi.org/10.5772/62162 |
| work_keys_str_mv | AT andreaspersson fastmatchingofbinarydescriptorsforlargescaleapplicationsinrobotvision AT amyloutfi fastmatchingofbinarydescriptorsforlargescaleapplicationsinrobotvision |