Browsing by Author "Chien, Been-Chian"

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A two-level relevance feedback mechanism for image retrieval
(2008-04-01)
Cheng, Pei-Cheng
;
Chien, Been-Chian
;
Ke, Hao-Ren
;
Yang, Wei-Pang
;
資訊工程學系
Content-based image retrieval (CBIR) is a group of techniques that analyzes the visual features (such as color, shape, texture) of an example image or image subregion to find similar images in an image database. Relevance feedback is often used in a CBIR system to help users express their preference and improve query results. Traditional relevance feedback relies on positive and negative examples to reformulate the query. Furthermore, if the system employs several visual features for a query, the weight of each feature is adjusted manually by the user or system predetermined and fixed by the system. In this paper we propose a new relevance feedback model suitable for medical image retrieval. The proposed method enables the user to rank the results in relevance order. According to the ranking, the system can automatically determine the importance ranking of features, and use this ranking to automatically adjust the weight of each feature. The experimental results show that the new relevance feedback mechanism outperforms previous relevance feedback models. (c) 2007 Elsevier Ltd. All rights reserved.
Classifier design with feature selection and feature extraction using layered genetic programming
(2008-02-01)
Lin, Jung-Yi
;
Ke, Hao-Ren
;
Chien, Been-Chian
;
Yang, Wei-Pang
;
資訊工程學系
This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP. (C) 2007 Elsevier Ltd. All rights reserved.
Combining textual and visual features for cross-language medical image retrieval
(2006)
Cheng, Pei-Cheng
;
Chien, Been-Chian
;
Ke, Hao-Ren
;
Yang, Wei-Pang
;
資訊工程學系
In this paper we describe the technologies and experimental results for the medical retrieval task and automatic annotation task. We combine textual and content-based approaches to retrieve relevant medical images. The content-based approach containing four image features and the text-based approach using word expansion are developed to accomplish these tasks. Experimental results show that combining both the content-based and text-based approaches is better than using only one approach. In the automatic annotation task we use Support Vector Machines (SVM) to learn image feature characteristics for assisting the task of image classification. Based on the SVM model, we analyze which image feature is more promising in medical image retrieval. The results show that the spatial relationship between pixels is an important feature in medical image data because medical image data always has similar anatomic regions. Therefore, image features emphasizing spatial relationship have better results than others.
Designing a classifier by a layered multi-population genetic programming approach
(2007-08-01)
Lin, Jung-Yi
;
Ke, Hao-Ren
;
Chien, Been-Chian
;
Yang, Wei-Pang
;
資訊工程學系
This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.