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- W2899290156 abstract "AbstractImage retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical reca..." @default.
- W2899290156 created "2018-11-09" @default.
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- W2899290156 date "2018-01-01" @default.
- W2899290156 modified "2023-09-26" @default.
- W2899290156 title "Modified one-class support vector machine for content-based image retrieval with relevance feedback" @default.
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- W2899290156 doi "https://doi.org/10.1080/23311916.2018.1541702" @default.
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