A comparison of feature detectors and descriptors for object class matching
Solid protocols to benchmark local feature detectors and descriptors were introduced by Mikolajczyk et al. 1,2. The detectors and the descriptors are popular tools in object class matching, but the wide baseline setting in the benchmarks does not correspond to class level matching where appearance variation can be large. We extend the benchmarks to the class matching setting and evaluate state of the art detectors and descriptors with Caltech and ImageNet classes. Our experiments provide important findings with regard to object class matching: (1) the original SIFT is still the best descriptor; (2) dense sampling outperforms interest point detectors with a clear margin; (3) detectors perform moderately well, but descriptors' performance collapses; (4) using multiple, even a few, best matches instead of the single best has significant effect on the performance; (5) object pose variation degrades dense sampling performance while the best detector (Hessian affine) is unaffected. The performance of the best detector descriptor pair is verified in the application of unsupervised visual class alignment where state of the art results are achieved. The findings help to improve the existing detectors and descriptors for which the framework provides an automatic validation tool.
Neurocomputing