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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Lu, Wei Lu, Hong-Tao Chung, Fu-Lai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China Center for Multimedia Signal Processing Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)078039092X

This paper presents a chaos-based robust digital image watermarking scheme in DWT domain. Because chaos is highly sensitive to its initial state, it can be used to permute the original image and make the image's spectrum spread. Entire DWT is applied into the permuted host image. Since the permuted image is a noise-like signal, the high frequency band after DWT is proper for watermark embedding. A binary image watermark is constructed by using chaotic map to embedded in the subband of DWT. Watermark detection is by computing the statistical correlation between the watermarked signal and the original watermark. Experimental results show good performance of the proposed scheme on resisting common watermarking attacks and geometric attacks. ©2005 IEEE.

关键词： Spread spectrum communication

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Chaos-based spread spectrum robust watermarking in DWT domain

Chaos-based spread spectrum robust watermarking in DWT domai...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Wei Lu Hong-Tao Lu Fu-Lai Chung Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Center for Multimedia Signal Processing and Department of Computing Hong Kong Polytechnic University Hong Kong China

关键词： Chaos Spread spectrum communication Robustness Watermarking Discrete wavelet transforms Chaotic communication Frequency Cryptography Digital images Data security

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Clustering by principal curve with tree structure

Clustering by principal curve with tree structure

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International Symposium on signals, Circuits and Systems (ISSCS)

作者： I. Cleju P. Franti Xiaolin Wu Multimedia Signal Processing Group Department of Computer and Information Science University of Karlsruhe Konstanz Germany Speech and Image Processing Research Group Department of Computer Science University of Joensuu Joensuu Finland Multimedia Computing and Comucations Laboratory Department of Etectrical and Computer Engineering McMaster University Hamilton ONT Canada

Data clustering is intensively used in signal processing in tasks such as multimedia compression, segmentation and pattern matching. In this work we extend the use of principal curves in clustering to complex multidimensional datasets. The use of principal curve in clustering is limited for high complexity data. Automatic parameterization of the principal curve to assure good results for different datasets is a difficult task. We propose to use the tree structure to capture the general settlement of the data. Using this topology, regions of the dataset can be extracted, individually clustered using the principal curve and then optimally recombined. The experiments show the improvement of the new method over the principal curve based clustering and the good performance compared to other clustering methods.

关键词： Tree data structures Clustering algorithms Partitioning algorithms multimedia computing Quantization Computer science Pattern matching Data mining Application software Information science

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract available

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Watermark extraction by magnifying noise and applying global minimum decoder

Watermark extraction by magnifying noise and applying global...

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International Conference on Image and Graphics (ICIG)

作者： Zhigeng Pan Li Li Mingmin Zhang D. Zhang State Key Laboratory of CAD&CG University of Zhejiang Hangzhou China Hangzhou Dianzi University Hangzhou China Multimedia Signal Processing and Department of Computing Polytechnic University Hong Kong China

For the classical watermark embedment model I = 1 + /spl alpha/W, the corresponding watermark detection has its limitation in its need of a fixed parameter for extracting watermarks. If the extraction parameter is too large, we cannot extract the watermark from the image that contains watermarks; if it is too small, the extracted watermarks may be blurred. This paper proposes a novel watermark extraction method. First, we treat the watermark information as noise for the watermarked image in its spatial domain. We then magnify the noise before detection. Next, we recover the watermark information by adjusting the extracted data from the frequency domain according to our global minimum method. Experimental results show that our watermark extraction method is more valid and accurate than the classical method. It can greatly reduce extraction errors.

关键词： Watermarking Decoding Data mining Frequency domain analysis Computer networks signal processing Embedded computing Electronic mail Computer errors Information security

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A public mesh watermarking algorithm based on addition property of Fourier transform

A public mesh watermarking algorithm based on addition prope...

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International Conference on Image and Graphics (ICIG)

作者： Li Li Zhigeng Pan David Zhang Hangzhou Dianzi University Hangzhou China State Key Laboratory of CAD&CG University of Zhejiang Hangzhou China Multimedia Signal Processing and Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

This paper presents a public mesh watermarking algorithm. First, the resultant watermarked image minus the original image is the watermark information. According to the addition property of the Fourier transform, a change of spatial domain will cause a change in the frequency domain. Then, the watermark information is scaled down and embedded in one part of the x-coordinate of the original mesh. Finally, the x-coordinate of the test mesh is amplified before extraction. Experimental results prove that our algorithm is resistant to a variety of attacks without the need for any preprocessing.

关键词： Watermarking Fourier transforms signal processing algorithms Data mining Frequency domain analysis Wavelet domain Partitioning algorithms Noise robustness Equations Electronic mail

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The impact of speech recognition errors on the effectiveness of spoken Cantonese query retrieval

The impact of speech recognition errors on the effectiveness...

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： T.K. Choi X.M. Zhu R.W.P. Luk F.L. Chung M.W. Mak K.M. Lam W.C. Siu Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China Centre for Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong China Department of Computing Hong Kong Polytechnic University Hong Kong China

This paper examines the impact of recognition errors on spoken Cantonese query retrieval effectiveness. One of the largest test collections provided by NTCIR for evaluating Chinese information retrieval is used. The retrieval system uses one of the best models (2-Poisson) and the robust bigram indexing strategy. If there are no syllable recognition errors, then the errors in converting spelling (called pinyin) to characters degrades the performance by 3.9% points which is not statistically significant. Otherwise, the performance dropped by 10.2% points which is statistically significant. We improved our system by merging the /n/ and /l/ phone labels and retrained the syllable-to-text conversion routines. The improved retrieval system dropped only 6.4% points.

关键词： Speech recognition Information retrieval Testing Character recognition Degradation Merging signal processing Robustness Indexing Writing

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Bark texture feature extraction based on statistical texture analysis

Bark texture feature extraction based on statistical texture...

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： Yuan-Yuan Wan Ji-Xiang Du De-Shuang Huang Zheru Chi Yiu-Ming Cheung Xiao-Feng Wang Guo-Jun Zhang Intelligent Computing Laboratory Institute of Intelligent Machines Chinese Academy and Sciences Hefei Anhui China Center for Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong China Department of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (纸本)0780386876

This paper quantitatively describes and discusses the usefulness of texture analysis methods for the recognition of bark. Comparative studies of bark texture feature extraction are performed for the four texture analysis methods such as the gray level run-length method (RLM), co-occurrence matrices method (COMM) and histogram method (HM) as well as auto-correlation method (ACM). Specifically, we use three classifiers of nearest neighbor (l-NN), k-nearest neighbor (k-NN) and moving median centers (MMC) hypersphere classifiers to verify the validity of the extracted bark texture features. To gain good results we added to color information that proved very efficient. Moreover, the experimental results also demonstrate that from the viewpoint of the recognition accuracy and computational complexity, the COMM method is superior to the other three methods.

关键词： Feature extraction Histograms Image texture analysis Image analysis Probability Machine intelligence signal processing Plants (biology) Entropy multimedia computing

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A hypersphere method for plant leaves classification

A hypersphere method for plant leaves classification

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： Guo-Jun Zhang Xiao-Feng Wang De-Shuang Huang Zheru Chi Yiu-Ming Cheung Ji-Xiang Du Yuan-Yuan Wan Intelligent Computing Laboratory Institute of Intelligent Machines Chinese Academy and Sciences Hefei Anhui China Center for Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong China Department of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (纸本)0780386876

The recognition of the tens of thousands of kinds of plants on Earth is an important and difficult task. If we use a nearest neighbour classifier to solve it, millions of training data are needed to obtain a high correct recognition rate. However, the corresponding classification process is quite time-consuming. We propose a new approach using a moving median centers hypersphere technique to recognize different plant leaves. With this new method, we can successfully decrease the classification time, and reduce the storage size without sacrificing the classification accuracy. Experimental results using real data show that this method is really efficient and effective in classifying different plant leaves.

关键词： Neural networks Training data Machine intelligence multimedia computing Biomedical signal processing Computer science Geoscience Earth Plants (biology) Agriculture

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