Improving CBIR Systems by Integrating Semantic Features
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Semantic Kernel Updating for Content-Based Image Retrieval
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Human-Centered Multimedia: Culture, Deployment, and Access
IEEE MultiMedia
An approach of multi-level semantics abstraction
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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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.