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- W1541649504 abstract "AbstractSemantics-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.KeywordsSupport Vector MachineImage RetrievalSupport Vector Machine ClassifierSemantic MeaningSemantic ClassifierThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1541649504 date "2005-01-01" @default.
- W1541649504 modified "2023-09-25" @default.
- W1541649504 title "An Approach of Multi-level Semantics Abstraction" @default.
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- W1541649504 doi "https://doi.org/10.1007/11552451_164" @default.
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