How does CBIR work with SVM instead of Euclidean distance?

Question

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:

1. Using knn for image retrieval;
2. Using svm for image retrieval.

How does it work?

Answer 1

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