Semi-supervised learning of object categories from paired local features

This paper presents a semi-supervised learning (SSL) approach to find similarities of images using statistics of local matches. SSL algorithms are well known for leveraging a large amount of unlabeled data as well as a small amount of labeled data to boost classification performance. Our approach proposes to formulate the problem of matching two images as an SSL based classification problem of image pairs with a minimal amount of labeled pairs. We apply a Gaussian random field model to represent each image pair as vertices in a weighted graph and the optimal configuration of the field is obtained by harmonic energy minimization. A symmetrical feature selection criterion is first introduced to select robust matches of local keypoints between two images. The Mallows distance is then adopted to combine multiple cues from statistics of local matches. Our experiments confirm that our SSL based approach not only boost classification performance but also improve robustness of the learned category model using only simple local keypoint features.