SVM (Support Vector Machine)
The feature vector generated by CNN is then consumed by the binary SVM which is trained on each class independently. This SVM model takes the feature vector generated in previous CNN architecture and outputs a confidence score of the presence of an object in that region. However, there is an issue with training with SVM is that we required AlexNet feature vectors for training the SVM class. So, we could not train AlexNet and SVM independently in paralleled manner. This challenge is resolved in future versions of R-CNN (Fast R-CNN, Faster R-CNN, etc.).
R-CNN | Region Based CNNs
Since Convolution Neural Network (CNN) with a fully connected layer is not able to deal with the frequency of occurrence and multi objects. So, one way could be that we use a sliding window brute force search to select a region and apply the CNN model to that, but the problem with this approach is that the same object can be represented in an image with different sizes and different aspect ratios. While considering these factors we have a lot of region proposals and if we apply deep learning (CNN) to all those regions that would computationally very expensive.
Ross Girshick et al in 2013 proposed an architecture called R-CNN (Region-based CNN) to deal with this challenge of object detection. This R-CNN architecture uses the selective search algorithm that generates approximately 2000 region proposals. These 2000 region proposals are then provided to CNN architecture that computes CNN features. These features are then passed in an SVM model to classify the object present in the region proposal. An extra step is to perform a bounding box regressor to localize the objects present in the image more precisely.