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  Weighted Substructure Mining for Image Analysis

Nowozin, S., Tsuda, K., Uno, T., Kudo, T., & BakIr, G. (2007). Weighted Substructure Mining for Image Analysis. In 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). Los Alamitos, CA, USA: IEEE Computer Society.

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Nowozin, S1, 2, Author           
Tsuda, K1, 2, Author           
Uno , T, Author
Kudo, T, Author
BakIr, G, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2007-06
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/CVPR.2007.383171
BibTex Citekey: 4452
 Degree: -

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Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007)
Place of Event: Minneapolis, MN., USA
Start-/End Date: 2007-06-17 - 2007-06-22

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Title: 2007 IEEE Conference on Computer Vision and Pattern Recognition
Source Genre: Proceedings
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Publ. Info: Los Alamitos, CA, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 8 Identifier: ISBN: 1-4244-1180-7