Browsing by Author "Xu, YY"

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A neural network based transcoder for MPEG2 video compression
(1999)
Fu, HC
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Chen, ZH
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Xu, YY
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Wang, CH
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資訊工程學系
In this paper, we proposed a neural network method for the high efficiency requatization in the design of a transcoder In our design, there are two types of video bitrate control in the proposed transcoder. One is the global adjusting of quantizer scales in which the adjusting is based on the complication of the whole frame, the other is the adaptive adjusting of quantizer scales, that the adjusting is the complication of the current macroblock. From our experimental results, the prototype transcoder can achieve desirable bitrate (1.5 Mbps) with an acceptable image quality. In additional, we constructed a video multiplexer for PPV or NVOD applications on the proposed transcoder.
A principal component based probabilistic DBNN for face recognition
(1996)
Shen, LJ
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Fu, HC
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Xu, YY
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Hsu, FR
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Chang, HT
A self-growing probabilistic decision-based neural network with automatic data clustering
(2004-10-01)
Tseng, CL
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Chen, YH
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Xu, YY
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Pao, HT
;
Fu, HC
In this paper, we propose a new clustering algorithm for a mixture of Gaussian-based neural network and self-growing probabilistic decision-based neural networks (SPDNN). The proposed self-growing cluster learning (SGCL) algorithm is able to find the natural number of prototypes based on a self-growing validity measure, Bayesian information criterion (BIC). The learning process starts from a single prototype randomly initialized in the feature space and grows adaptively during the learning process until most appropriate number of prototypes are found. We have conducted numerical and real-world experiments to demonstrate the effectiveness of the SGCL algorithm. In the results of using SGCL to train the SPDNN for data clustering and speaker identification problems, we have observed a noticeable improvement among various model-based or vector quantization-based classification schemes. (C) 2004 Elsevier B.V. All rights reserved.
A WWW-based intelligent multimedia information query and retrieve system
(2000)
Xu, YY
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Fu, HC
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Pao, HT
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資訊工程學系
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Department of Computer Science
In this paper, we propose and implement a World Wide Web based video query system: the intelligent Multimedia Information Processing System (IMIPS). The IMIPS first collects videos available over the WWW, analyzes these videos to extract keyframes, and indexes these keyframes at a local database. By using the proposed Multiple-Instance learning algorithm, a user can select a few keyframes to train a desired image query model. When a query model is selected, the proposed IMIPS will retrieve marched images from the local database, and display them as a small icon at the user interface. Then a user can click on the interested icon to retrieve and to play the video from the original web site. According to the implemented prototype system, a user can find one's desired videos on the WWW with only a few (2-3) times of retraining to achieve 70% hit rate on 50 retrieved videos among 5 different query models.
Automated information. mining on multimedia TV news archives
(2005)
Lai, PS
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Cheng, SS
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Sun, SY
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Huang, TY
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Su, JM
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
Forecasting electricity market prices: A neural network based approach
(2004)
Xu, YY
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Hsieh, R
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Lu, Y
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Shen, YC
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Chuang, SC
This paper presents a neural network approach to forecast the Phelix Base (PB) electricity market prices for European Energy Exchange (EEX). Up to now there has been little scientific work on forecasting the price development on the electricity markets. In this study, the Phelix Base moving average (PBMA), the moving difference (PBMD), and multilayer feedforward neural networks (MLNN) are used to predict various period for 7, 14, 21, 28, 63, 91, 182, and 273 days ahead of electric prices. The experimental results of forecasting by MLNNs and linear methods (autoregressive error model) are compared and discussed. The MLNNs outperform from 11.4% to 64.6% superior to the traditional linear regression method. It seems that the proposed MLNN can be very useful in predicting the electricity market prices of EEX.
Multi-linguistic handwritten character recognition by Bayesian decision-based neural networks
(1997)
Fu, HC
;
Xu, YY
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交大名義發表
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資訊工程學系
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National Chiao Tung University
This paper proposes a multi-linguistic handwritten characters recognition system based on Bayesian decision-based neural networks (BDNN). The proposed system consists of two modules: First, a coarse classifier determines an input character to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image to its most matched reference character in the subclass. The proposed BDNN can be effectively applied to implement all these modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. Our prototype system demonstrates a successful utilization of BDNN to handwriting of Chinese and alphanumeric character recognition on both the public databases (HCCR/CCL for Chinese and CEDAR for the alphanumerics) and in house database (NCTU/NNL). Regarding the performance, experiments on three different databases all demonstrated high recognition (88 similar to 92%) accuracies as well as low rejection/acceptance (6.7%) rates, as elaborated in Section 3.2. As to the processing speed, the whole recognition process (including image preprocessing, feature extraction, and recognition) consumes approximately 0.27second/character on a Pentium-90 based personal computer, without using hardware accelerator or co-processor.
Multilinguistic handwritten character recognition by Bayesian decision-based neural networks
(1998-10-01)
Fu, HC
;
Xu, YY
;
資訊工程學系
;
Department of Computer Science
In this paper, we present a Bayesian decision-based neural network (BDNN) for multilinguistic handwritten character recognition, The proposed self-growing probabilistic decision-based neural network (SPDNN) adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme, Our prototype system demonstrates a successful utilization of SPDNN to the handwriting of Chinese and alphanumeric character recognition on both public databases (CCL/HCCR1 for Chinese and CEDAR for the alphanumerics) and in-house database (NCTU/NNL), Regarding the performance, experiments on three different databases all demonstrated high recognition (86-94%) accuracy as well as low rejection/acceptance (6.7%) rates. As for the processing speed, the whole recognition process (including image preprocessing, feature extraction, and recognition) consumes approximately 0.27 s/character on a Pentium-100 based personal computer, without using a hardware accelerator or coprocessor.
Multilinguistic handwritten character recognition by Bayesian decision-based neural networks
(1998-10-01)
Fu, HC
;
Xu, YY
;
資訊工程學系
;
Department of Computer Science
In this paper, we present a Bayesian decision-based neural network (BDNN) for multilinguistic handwritten character recognition, The proposed self-growing probabilistic decision-based neural network (SPDNN) adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme, Our prototype system demonstrates a successful utilization of SPDNN to the handwriting of Chinese and alphanumeric character recognition on both public databases (CCL/HCCR1 for Chinese and CEDAR for the alphanumerics) and in-house database (NCTU/NNL), Regarding the performance, experiments on three different databases all demonstrated high recognition (86-94%) accuracy as well as low rejection/acceptance (6.7%) rates. As for the processing speed, the whole recognition process (including image preprocessing, feature extraction, and recognition) consumes approximately 0.27 s/character on a Pentium-100 based personal computer, without using a hardware accelerator or coprocessor.
Multimedia TV news browsing system
(2004)
Xu, YY
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Chen, YH
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Tseng, CL
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Lai, PS
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Fu, HC
In this demo, we present a system we have developed for automatic broadcast-quality video indexing that successfully combines results from the fields of speaker verification, acoustic analysis, very large vocabulary caption character recognition, content based sampling of video, information retrieval, dialogue systems, and ASF media delivery over IP. The prototype system of this demo is available at http://nn.csie.nctu.edu.tw/Project1-1/intruduction.htm.
Neural network based image retrieval with multiple instance leaning techniques
(2005)
Chuang, SC
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Xu, YY
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Fu, HC
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資訊工程學系
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Department of Computer Science
In this paper, we propose a Generalized Probabilistic decision based Neural Network (GPDNN) for content-based image retrieval (CBIR). Instead of receiving the numerical values of each data points as the input, the proposed GPDNN models the I/O relationship via the distribution of input data and their corresponding outputs. The GPDNN involves the Multiple-Instance learning techniques to learn a desired concept. A set of exemplar images are selected by a user, each of which is labeled as conceptual related (positive) or conceptual unrelated (negative) image. Then, by using the proposed learning algorithm, an image classification system can learn the user's preferred image class from the positive and negative examples. The experimental results show that for only a few times of relearning, a user can use the prototype system to retrieve favor images from the database.
Recognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networks
(1998)
Fu, HC
;
Xu, YY
;
資訊工程學系
;
Department of Computer Science
In this paper, we present a Bayesian decision-based neural networks (BDNN) for handwritten Chinese character recognition. The proposed Self-growing Probabilistic Decision-based Neural Networks (SPDNN) adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Our prototype system demonstrates a successful utilization of SPDNN to the handwriting of Chinese character recognition on the public databases, CCL/HCCR1 and in house database (NCTU/NNL). Regarding the performance, experiments on three different databases all demonstrated high recognition (86 similar to 94%) accuracy as well as low rejection/acceptance (6.7%) rates. As to the processing speed, the whole recognition process (including image preprocessing feature extraction, and recognition) consumes approximately 0.27 second/character on a Pentium-100 based personal computer, without using hardware accelerator or co-processor.
Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural networks
(1998)
Fu, HC
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Xu, YY
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資訊工程學系
;
Department of Computer Science
Recognition of similar (confusion) characters is a difficult pattern recognition problem. In this paper, we introduce neural network solution, that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Networks (SPDNN) is a probabilistic type neural networks, which adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we constructed a three stage recognition system. The prototype system demonstrates a successful utilization of SPDNN to similar handwritten Chinese recognition on the public database CCL/HCCR1 (5401 characters x200 samples). Regarding the performance, experiments on the CCL/HCCR1 database demonstrated a 90.12% of recognition accuracy with no rejection, and 94.11% of accuracy with 6.7% rejection rates respectively.
Unconstrained freehand-written Chinese characters recognition by Self-growing Probabilistic Decision-based Neural Networks
(1998)
Fu, HC
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Xu, YY
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Lee, YP
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資訊工程學系
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Department of Computer Science
This paper presents the design of Self-growing Probabilistic Decision-based Neural Networks (SPDNN) for the recognition of unconstrained freehand-written Chinese characters. In this esearch, the authors have developed: (1) an SPDNN based personal handwriting adaptive methodologies, (2) a two stage recognition structure: (a) a handprinted character recognizer, and (b) a personal adaptive freehand-written Chinese character recognizer, on a personal computer. For the unconstrained human handwriting, most of the reported handwriting recognition systems performed poorly (recognition rate falls between 40% and 50%). The proposed system shows significant improvement on the recognition rates through adaptive learning. The average recognition rates was raised from 44.09% to 82.2% in 5 learning cycles. And the performance could finally be increased up to 90.03% in 10 learning cycles.
User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks
(2000-11-01)
Fu, HC
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Chang, HY
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Xu, YY
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Pao, HT
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資訊工程學系
It is generally agreed that, for a given handwriting recognition task, a user dependent system usually outperforms a user independent system, as long as a sufficient amount of training data is available. When the amount of user training data is limited, however, such a performance gain is not guaranteed. One way to improve the performance is to make use of existing knowledge, contained in a rich multiuser data base, so that a minimum amount of training data is sufficient to initialize a model for the new user, We mainly address the user adaption issues for a handwriting recognition system, Based on self-growing probabilistic decision-based neural networks (SPDNNs), user adaptation of the parameters of SPDNN is formulated as incremental reinforced and antireinforced learning procedures, which are easily integrated into the batched training procedures of the SPDNN, In this study, me developed 1) an SPDNN based handwriting recognition system; 2) a two-stage recognition structure; and 3) a three-phase training methodology for a) a global coarse classifier (stage 1); b) a user independent hand written character recognizer (stage 2); and c) a user adaptation module on a personal computer. With training and testing on a 600-word commonly used Chinese character set, the recognition results indicate that the user adaptation module significantly improved the recognition accuracy The average recognition rate increased from 44.2% to 82.4% in five adapting cycles, and the performance could finally increase up to 90.2% in ten adapting cycles.