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dc.contributor.authorChuang, Shun-Chinen_US
dc.contributor.authorXu, Yeong-Yuhen_US
dc.contributor.authorFu, Hsin Chiaen_US
dc.contributor.authorHuang, Hsiang-Chehen_US
dc.description.abstractIn this paper, we proposed a Multiple-Instance Neural Network (MINN) for content-based image retrieval (CBIR). In order to represent the rich content of an image without precisely image segmentation, the image retrieval problem is considered as a multiple-instance learning problem. A set of exemplar images are selected by a user, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. Then, the proposed MINN is trained by using the proposed learning algorithm to learn the user's preferred image concept from the positive and negative examples. Experimental results show that: (1) without image segmentation and using only the color histogram as the image feature, the MINN without relevance feedback performs slightly inferior to some leading image retrieval methods, and (2) the MINN with the relevance feedback can significantly improve the retrieving performance from 40.3% to 59.3%, which outperforms to the results of some leading image retrieval methods.en_US
dc.titleA multiple-instance neural networks based image content retrieval systemen_US
dc.typeProceedings Paperen_US
dc.identifier.journalICICIC 2006: First International Conference on Innovative Computing, Information and Control, Vol 2, Proceedingsen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
Appears in Collections:Conferences Paper