@inproceedings{4452,
  title = {Weighted Substructure Mining for Image Analysis},
  journal = {Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007)},
  booktitle = {CVPR 2007},
  abstract = {In web-related applications of image categorization, it is
  desirable to derive an interpretable classification rule with
  high accuracy. Using the bag-of-words representation and
  the linear support vector machine, one can partly fulfill the
  goal, but the accuracy of linear classifiers is not high and
  the obtained features are not informative for users. We propose
  to combine item set mining and large margin classifiers
  to select features from the power set of all visual words.
  Our resulting classification rule is easier to browse and simpler
  to understand, because each feature has richer information.
  As a next step, each image is represented as a graph
  where nodes correspond to local image features and edges
  encode geometric relations between features. Combining
  graph mining and boosting, we can obtain a classification
  rule based on subgraph features that contain more information
  than the set features. We evaluate our algorithm
  in a web-retrieval ranking task where the goal is to reject
  outliers from a set of images returned for a keyword
  query. Furthermore, it is evaluated on the supervised classification
  tasks with the challenging VOC2005 data set.
  Our approach yields excellent accuracy in the unsupervised
  ranking task compared to a recently proposed probabilistic
  model and competitive results in the supervised classification task.},
  pages = {1-8},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = jun,
  year = {2007},
  slug = {4452},
  author = {Nowozin, S. and Tsuda, K. and Uno, T. and Kudo, T. and BakIr, G.},
  month_numeric = {6}
}