PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Weighted Substructure Mining for Image Analysis
Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo and Goekhan BakIr
In: IEEE Computer Vision and Pattern Recognition (CVPR2007), Jun 2007, Minneapolis, USA.


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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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Software and dataset available at
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:3100
Deposited By:Sebastian Nowozin
Deposited On:19 December 2007