Weighted Substructure Mining for Image Analysis

1 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...

• Type: Conference
• Year: 2007
• Where: MLG
• Authors: Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo, Gökhan H. Bakir
• Keywords: Classification Rule, Interpretable Classification Rule, Machine Learning, MLG 2007, Supervised Classification Task