document: Mining images on semantics via statistical learning In this paper , we have proposed a novel framework to enable hierarchical image classification via statistical learning . By integrating the concept hierarchy for semantic image concept organization , a hierarchical mixture model is proposed to enable multi-level modeling of semantic image concepts and hierarchical classifier combination . Thus , learning the classifiers for the semantic image concepts at the high level of the concept hierarchy can be effectively achieved by detecting the presences of the relevant base-level atomic image concepts . To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy , we have proposed a novel adaptive EM algorithm to achieve more effective model selection and parameter estimation . In addition , a novel penalty term is proposed to effectively eliminate the misleading effects of the outlying unlabeled images on semi-supervised classifier training . Our experimental results in a specific image domain of outdoor photos are very attractive .
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