Region Based Convolutional Neural Networks: Difference between revisions

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=== R-CNN ===

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

Given an input image, R-CNN begins by applying selective search to extract [[Region of interest|regions of interest]] (ROI), where each ROI is a rectangle that may represent the boundary of an object in image. Depending on the scenario, there may be as many as {{nobr|two thousand}} ROIs. After that, each ROI is fed through a neural network to produce output features. For each ROI's output features, an ensemble of [[support-vector machine]] classifiers is used to determine what type of object (if any) is contained within the ROI.<ref name=":2">{{Cite journal |last1=Girshick |first1=Ross |last2=Donahue |first2=Jeff |last3=Darrell |first3=Trevor |last4=Malik |first4=Jitendra |date=2016-01-01 |title=Region-Based Convolutional Networks for Accurate Object Detection and Segmentation |url=https://ieeexplore.ieee.org/document/7112511 |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=38 |issue=1 |pages=142–158 |doi=10.1109/TPAMI.2015.2437384 |pmid=26656583 |issn=0162-8828|url-access=subscription }}</ref>

=== Fast R-CNN ===