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I match SIFT descriptors between image pairs. The images are Figures from scientific publications. In the next step I filter the detected matches using RANSAC. That works well, but some images have two objects that match, therefore two models are required. However, RANSAC detect only the most dominant one and treats the second one as outlier.

Is there anyway to detect both object? Maybe there is a way to cluster matched SIFT descriptors beforehand and only apply RANSAC between detected clusters?

In this example only one object is detected and the second one (green rectangle) is treated as outlier.

I'm searching for duplicates within a group of images which where extracted from scientific papers. The problem is that some of those images have enumerations, i.e. "A", "B", ... which leads to a high false positive rate. I'm using SIFT for detection and description with ratio test, cross check validation and RANSAC for match filtering. But there are still a lot of false positives left.

Here three examples of unwanted matches:

Any ideas how I can remove those false duplicates with minimal negative influence regarding the true positive rate?