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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.
