Browsing by Author "Chuang, Shun-Chin"

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A multiple-instance neural networks based image content retrieval system
(2006)
Chuang, Shun-Chin
;
Xu, Yeong-Yuh
;
Fu, Hsin Chia
;
Huang, Hsiang-Cheh
;
資訊工程學系
In 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.
Weighted bootstrap for neural model selection
(2008)
Chuang, Shun-Chin
;
Hung, Wen-Liang
;
Fu, Hsin-Chia
;
資訊工程學系
;
Department of Computer Science
This article proposes a weighted bootstrap procedure, which is an efficient bootstrap technique for neural model selection. Our primary interest in reducing computer effort is to not resample (in the original bootstrap procedure) uniformly from the original sample, but to modify this distribution in order to obtain variance reduction. The performance of the weighted bootstrap is demonstrated on two artificial data sets and one real dataset. Experimental results show that the weighted bootstrap procedure permits an approximately 2 to 1 reduction in replication size.