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I am currently working on a projet to perform image recognition. There is a big set of images and I have to predict whether or not an image contains given characteristics. For example, the output could be whether or not there is a banana in the picture.
I would like to implement a classifier using SVM with output yes or no the image contains the given characteristics. What is the simplest way to train a SVM classifier on images with 2 outputs? Is there any template to use in Python? Thanks a lot.
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The main advantage of SVM is that it can be used for both classification and regression problems. SVM draws a decision boundary which is a hyperplane between any two classes in order to separate them or classify them. SVM also used in Object Detection and image classification.
Detection of ships in satellite imagery successfully uses machine learning and computer vision algorithms. By comparing SVM and CNN on this data, it can be seen that CNN has higher accuracy and is considered better in detecting ship objects. CNN has more steps so that the time needed to run it is longer than SVM does.
SVM: Support Vector Machine is a supervised classification algorithm where we draw a line between two different categories to differentiate between them. SVM is also known as the support vector network.
The SVM-Linear, SVM-RBF and CNN model is used to extract useful high-level features automatically given that it provides results comparable with each other, including hyperspectral image classification.
With SVM you can classify set of images.For example You can train svm with set of car and plane images.Once you trained it can predict the class of an unknown images as whether it is car or plane.There is also multiclass SVM.
In your case,Make two sets of images for training SVM
Once your training phase completed it will output to which class the given image belong.If its in banana class you can output as Yes otherwise No.
Usefull links
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