I'm currently working on a "Where's Waldo" project as part of my coursework, where I have to find 3 different characters in any given image - Waldo, Wenda, and Wizard.
I'm trying to convert this into a machine learning problem where I essentially train an SVM with templates of each character (so I have 3 classes/labels), but I'm not so sure what the best approach is when it comes to building the model.
Before training, I partitioned the templates of each character into training and validation sets, and my SVM performs well (~82% accuracy in predicting whether a window/template is Waldo/Wenda/Wizard). However, the testing accuracy is rather low, as the final windows that I get are false positives.
Would it be better to add a 4th label that says 'None of the above' to reduce the percentage of false positives, or is that only useful for when we want Yes/No classification?
Any input would be appreciated!