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Chapter 5
A more practical approach would be use a tool such as Selective Search (Selective
Search for Object Recognition, by Uijlings et al, http://www.huppelen.nl/
publications/selectiveSearchDraft.pdf), which uses traditional computer
vision techniques to find areas in the image that might contain objects. These regions
are called "Region Proposals," and the network to detect them was called "Region
Proposal Network," or R-CNN. In the original R-CNN, the regions were resized
and fed into a network to yield image vectors:
Figure 4: Region extraction and warped region as described in "Rich feature hierarchies for accurate
object detection and semantic segmentation", Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik,
UC Berkeley.
These vectors were then classified with an SVM-based classifier (https://
en.wikipedia.org/wiki/Support-vector_machine), and the bounding boxes
proposed by the external tool were corrected using a linear regression network over
the image vectors. A R-CNN network can be represented conceptually as shown in
Figure 5:
Figure 5: R-CNN network
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