Training inter-related classifiers for automatic image classification and annotation

A structural learning algorithm is developed in this paper to achieve more effective training of large numbers of inter-related classifiers for supporting large-scale image classification and annotation. A visual concept network is constructed for characterizing the inter-concept visual correlations intuitively and determining the inter-related learning tasks automatically in the visual feature space rather than in the label space. By partitioning large numbers of object classes and image concepts into a set of groups according to their inter-concept visual correlations, the object classes and image concepts in the same group will share similar visual properties and their classifiers are strongly inter-related while the object classes and image concepts in different groups will contain various visual properties and their classifiers can be trained independently. By leveraging the inter-concept visual correlations for inter-related classifier training, our structural learning algorithm can train the inter-related classifiers jointly rather than independently, which can enhance their discrimination power significantly. Our experiments have also provided very positive results on large-scale image classification and annotation.

► A visual concept network is constructed to measure the inter-concept similarity.
► Large numbers of classes are automatically partitioned into a set of groups.
► The inter-related learning tasks are determined from the visual feature space.
► Inter-related classifiers are trained jointly rather than independently.
► We perform our experiments on large-scale image set for algorithm evaluation.