Generally CBIR works with Euclidean distance for comparing a query image and a database image feature vectors.
However in math works, I got a source code that instead of Euclidean distance it is done with SVM, like a content based image retrieval using two techniques:
- Using knn for image retrieval;
- Using svm for image retrieval.
How does it work?
There are some literature in that area:
Content Based Image Retrieval Using SVM Algorithm
An Approach for Image Retrieval Using SVM
Image Retrieval with Structured Object Queries Using Latent Ranking SVM
As far as I know, the simple approach is having a feature extraction phase (i.e Using PCA) and then doing a one-class svm classification.
K-NN usually uses euclidean distance anyway so the algorithm is offering you a more consistent decision boundary and a feature extraction phase on top of that. You can see an example here