SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
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
Content-Based Hierarchical Classification of Vacation Images
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
User-Centered image semantics classification
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Semantics-based image retrieval is a challenging problem. In this paper, we propose an approach for image semantics abstraction, which constructs a multi-level semantics tree based on human subject and train hierarchical semantic classifier. According to our method, image features are selected by using priori knowledge. Then, those images are classified in every level by the classifier based on support vector machines (SVM). The SVM classifiers learn the semantics of specified classes from a training database of image. Experiments show that we can abstract multi-level semantics from image database by using less low-level features.