Exploiting feature correspondence constraints for image recognition

Image recognition is one of the fundamental problems in multimedia analysis. Typically in the training database, there will be more than one image for each object, however most existing bag-of-features based approaches treat them independently and completely ignore the feature correspondence relationship among them. As a result, features corresponding to the same physical point may be clustered into different clusters, which finally leads to inaccurate image representations for recognition. To tackle the problem, we present a supervised codebook construction algorithm exploiting the feature correspondence constraints in feature clustering. Features in different images of the same object are first matched, then ho-mography between images are computed to remove outliers as well as recover the feature correspondences that are not correctly matched. Features belonging to the same physical point are enforced to be in the same cluster. We show via experiments that codebook constructed using this approach can improve the recognition performance.