Browsing by Author "Cho, CW"

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A new scene analysis using genetic algorithm based fuzzy ID3 method
(2005)
Chang, JY
;
Cho, CW
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we utilize a neural network based machine learning algorithm to segment natural objects in outdoor scene images. We have developed a genetic algorithm based fuzzy ID3 method, which can build a fuzzy decision tree to summarize the regularities existing in the data set. Using this method, we then propose a road scene analysis system, by which natural element segmentation rules can be learned from several road scene images. In the image analysis phase, the natural element regions are obtained through inference on these learned rules. Moreover, we can apply image groundtruthing to further improve the classification accuracy. The testing results have demonstrated that the object segmentation accuracy is quite high.
Genetic algorithm based fuzzy ID3 algorithm
(2004)
Chang, JY
;
Cho, CW
;
Hsieh, SH
;
Chen, ST
;
電控工程研究所
In this paper, we propose a genetic algorithm (GA) based fuzzy ID3 algorithm to construct a fuzzy classification system with both high classification accuracy and compact rule base size. This goal is achieved by two key steps. First, we optimize by GA the parameters controlling the means and variances of fuzzy membership functions and leaf node conditions for tree construction. Second, we prune the rules of the tree constructed by evaluating the effectiveness of the rule, and the remaining rules are retrained by the same GA proposed. Our proposed scheme is tested on various famous data sets, and its results is compared with C4.5 and IRID3. Simulation result shows that our proposed scheme leads to not only better classification accuracy but also smaller size of rule base.
Scene analysis system using a combined fuzzy logic-based technique
(2002-05-01)
Chang, JY
;
Cho, CW
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a two-stage scene analysis scheme using a combined fuzzy logic-based technique. The first stage begins with generating fuzzy rules to describe the scene. Based on these fuzzy classification rules. each image pixel is inferred and then classified to the natural object category with the largest membership degree. The second stage involves a newly derived fuzzy K-nearest neighbor algorithm that further refines the classification result obtained. With this second stage, the proposed system is robust because it is demonstrated to be insensitive to the variations of membership functions and image noise contamination. Simulations of real world images have shown that the proposed scheme is very successful and the results are visually confirmed by human observation. The satisfactory results achieved in this paper suggest the feasibility of developing similar systems for other types of images aiming at image description problems.
Second-order asymmetric BAM design with a maximal basin of attraction
(2003-07-01)
Chang, JY
;
Cho, CW
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Bidirectional associative memory (BAM) generalizes the associative memory (AM) to be capable of performing two-way recalling of pattern pairs. Asymmetric bidirectional associative memory (ABAM) is a variant of BAM relaxed with connection-weight symmetry restriction and enjoys a much better performance than a conventional BAM structure. Higher-Order associative memories (HOAMs) are reputed for their higher memory capacity than the first-order counterparts, yet there are few HOAMs design schemes proposed up to date. To this end, we are concerned in this paper with designing a second-order asymmetric bidirectional associative memory (SOABAM) with a maximal basin of attraction, whose extension to a HOABAM is possible and straightforward. First, a necessary and sufficient condition is derived for the connection weight matrix of SOABAM that can guarantee the recall of all prototype pattern pairs. To respect the complete recall theorem, an adaptive local training rule, which is adaptive in the learning step size and updates only the entries in the connection weight related to the most needful bit of a prototype, is formulated and it leads to better results and faster design. Then derived is a theorem, designing a SOABAM further enlarging the quantities required to meet the complete recall theorem will enhance the capability of evolving a noisy pattern to converge to its association pattern vector without error. Based on this theorem, our algorithm is also modified to ensure each training pattern is stored with a basin of attraction as large as possible. Computer simulations over the color graphics adapter (CGA) fonts have demonstrated the superiority of the proposed local training rule over other prevailing BAM schemes.