User-Centered Image Semantics Classification
In this paper, we propose a multiple-level image semantics classification method. The multiple-level image semantics classifier is constructed according to a hierarchical semantics tree. A semantics tree is defined according to the individual user’s habit of managing files. So it is personalized. The classification features are selected by calculating information entropy of images. The hierarchical classifier is constructed according to a class correlation measure. This measure considers both the relation of the classifiers between different hierarchical levels and the relation between the classifiers at the same level. The unlabelled pictures can be classified top-down and assigned to corresponding class and semantic labels. In our experiment binary SVM is used. The hierarchical classifier is built by selecting meta-classifiers with the combinations that have better performance. The result shows that the hierarchical classifier is more effective than a flat method.
KeywordsSemantic Classifier Water Lily Semantic Tree Hierarchical Classifier Category Hierarchy
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