Skip to main content
Article thumbnail
Location of Repository

Semi-Supervised Learning of Object Categories from Paired Local Features

By Wen Wu and Jie Yang


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

Topics: Object Classification
Year: 2011
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.