Weighted Substructure Mining for Image Analysis

2007

Conference Paper

ei


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.

Author(s): Nowozin, S. and Tsuda, K. and Uno, T. and Kudo, T. and BakIr, G.
Book Title: CVPR 2007
Journal: Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007)
Pages: 1-8
Year: 2007
Month: June
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Los Alamitos, CA, USA
Digital: 0
DOI: 10.1109/CVPR.2007.383171
Event Name: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event Place: Minneapolis, Minn., USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{4452,
  title = {Weighted Substructure Mining for Image Analysis},
  author = {Nowozin, S. and Tsuda, K. and Uno, T. and Kudo, T. and BakIr, G.},
  journal = {Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007)},
  booktitle = {CVPR 2007},
  pages = {1-8},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = jun,
  year = {2007},
  month_numeric = {6}
}