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- W2352757390 abstract "Semantic image retrieval has been a crux to bridge semantic gap between the simple visual features and the advance semantics delivered by an image. In this paper, an image retrieval method combining SVM and Adaboost algorithm is proposed. The proposed approach selects the most informative samples in database to train SVM, it can reduce the feedback rounds and the number of samples effectively, and it can use both advantages to improve the accuracy of image retrieval. At last Adaboost method is proposed to integrate studying with SVM, and it improves the image retrieval performance. The experiments show that our method works well in solving the small sample size problem and it can improve the retrieval efficiency and performance consistently under the condition of limited training samples." @default.
- W2352757390 created "2016-06-24" @default.
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- W2352757390 date "2012-01-01" @default.
- W2352757390 modified "2023-09-27" @default.
- W2352757390 title "The Application of SVM and Adaboost in Image Retrieval" @default.
- W2352757390 hasPublicationYear "2012" @default.
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