Region Based Convolutional Neural Networks: Difference between revisions

Revision as of 16:26, 2 May 2025

Revision as of 03:33, 25 May 2025

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{{Short description|Machine learning model family}}

[[File:R-cnn.svg|thumb|272x272px|R-CNN architecture]]

'''Region-based Convolutional Neural Networks (R-CNN)''' are a family of machine learning models for [[computer vision]], and specifically [[object detection]] and localization.<ref name=":0">{{Cite book |last1=Zhang |first1=Aston |title=Dive into deep learning |last2=Lipton |first2=Zachary |last3=Li |first3=Mu |last4=Smola |first4=Alexander J. |date=2024 |publisher=Cambridge University Press |isbn=978-1-009-38943-3 |___location=Cambridge New York Port Melbourne New Delhi Singapore |chapter=14.8. Region-based CNNs (R-CNNs) |chapter-url=https://d2l.ai/chapter_computer-vision/rcnn.html}}</ref> The original goal of R-CNN was to take an input image and produce a set of [[Minimum bounding box|bounding boxes]] as output, where each bounding box contains an object and also the category (e.g. car or pedestrian) of the object. In general, R-CNN architectures perform selective search<ref name=":1">{{Cite journal |last1=Uijlings |first1=J. R. R. |last2=van de Sande |first2=K. E. A. |last3=Gevers |first3=T. |last4=Smeulders |first4=A. W. M. |date=2013-09-01 |title=Selective Search for Object Recognition |url=https://link.springer.com/article/10.1007/s11263-013-0620-5 |journal=International Journal of Computer Vision |volume=104 |issue=2 |pages=154–171 |doi=10.1007/s11263-013-0620-5 |issn=1573-1405|url-access=subscription }}</ref> over feature maps outputted by a CNN.

R-CNN has been extended to perform other computer vision tasks, such as: tracking objects from a drone-mounted camera,<ref>{{Cite news |last=Nene |first=Vidi |date=Aug 2, 2019 |title=Deep Learning-Based Real-Time Multiple-Object Detection and Tracking via Drone |url=https://dronebelow.com/2019/08/02/deep-learning-based-real-time-multiple-object-detection-and-tracking-via-drone/ |access-date=Mar 28, 2020 |work=Drone Below}}</ref> locating text in an image,<ref>{{Cite news |last=Ray |first=Tiernan |date=Sep 11, 2018 |title=Facebook pumps up character recognition to mine memes |url=https://www.zdnet.com/article/facebook-pumps-up-character-recognition-to-mine-memes/ |access-date=Mar 28, 2020 |publisher=[[ZDNET]]}}</ref> and enabling object detection in [[Google Lens]].<ref>{{Cite news |last=Sagar |first=Ram |date=Sep 9, 2019 |title=These machine learning methods make google lens a success |url=https://analyticsindiamag.com/these-machine-learning-techniques-make-google-lens-a-success/ |access-date=Mar 28, 2020 |work=Analytics India}}</ref>

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=== Selective search ===

Given an image (or an image-like feature map), '''selective search''' (also called Hierarchical Grouping) first segments the image by the algorithm in (Felzenszwalb and Huttenlocher, 2004),<ref>{{Cite journal |last1=Felzenszwalb |first1=Pedro F. |last2=Huttenlocher |first2=Daniel P. |date=2004-09-01 |title=Efficient Graph-Based Image Segmentation |url=https://link.springer.com/article/10.1023/B:VISI.0000022288.19776.77 |journal=International Journal of Computer Vision |language=en |volume=59 |issue=2 |pages=167–181 |doi=10.1023/B:VISI.0000022288.19776.77 |issn=1573-1405|url-access=subscription }}</ref> then performs the following:<ref name=":1" />

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Input: (colour) image