Evaluation of Binary Descriptors for Fast and Fully Automatic Identification

In this study we evaluate the potential of local binary descriptors for automatic sorting in an industrial context. This problem is different from that of retrieval for human handling as we need to identify the one correct class, rather than finding all the similar classes. We have looked at classes of objects that need to be identified by their cover or label, rather than their shape. Challenges for this application are that the process needs to be very fast and the approach must be able to distinguish between a large number of classes, where the classes can be quite similar and have identical elements. We have studied various combinations of detectors and binary descriptors in combination with approximate nearest neighbor (ANN) searches in such contexts. Our conclusion is that these approaches are well suited for this type of automatic sorting, and our experiments show that for the best performing combinations we are able to obtain a 99% recognition rate on a database of 80,000 images using an average of less than 0.5 seconds per image.