{"title":"A GA-based clustering algorithm for large data sets with mixed and categorical values","authors":"Li Jie, G. Xinbo, Jiao Li-cheng","doi":"10.1109/ICCIMA.2003.1238108","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238108","url":null,"abstract":"In the field of data mining, it is often encountered to perform cluster analysis on large data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. For this purpose, this paper presents a novel clustering algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. Experimental result illustrates that the GA-based new clustering algorithm is feasible for the large data sets with mixed numeric and categorical values.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Image retrieval based on multiple features using wavelet","authors":"Tian Yumin, Mei Lixia","doi":"10.1109/ICCIMA.2003.1238114","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238114","url":null,"abstract":"An algorithm of image retrieval using both color feature and texture feature is proposed in this paper. Circular region energy in low frequency band of wavelet transform of an image is used as color feature of the image and the synthesize energy in high frequency bands of multi-scale wavelet transforms is used as texture feature. A method using the linear combination of several single features is applied for image retrieval based multiple features, in which the weight of every feature is determined self-adaptively, moreover, relevance feedback technique is used to adjust the weights in order that the retrieval results accord with the user's retrieval goal gradually.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121187667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Segmentation of MR osteosarcoma images","authors":"Jincheng Pan, Minglu Li","doi":"10.1109/ICCIMA.2003.1238155","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238155","url":null,"abstract":"There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132562179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shih-Pin Chao, Chih-Yi Chiu, J. Chao, Yil-Cheng Ruan, Shin-Nine Yang
{"title":"Motion retrieval and synthesis based on posture features indexing","authors":"Shih-Pin Chao, Chih-Yi Chiu, J. Chao, Yil-Cheng Ruan, Shin-Nine Yang","doi":"10.1109/ICCIMA.2003.1238136","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238136","url":null,"abstract":"This paper presents a simple and effective approach to synthesize new motions from a given sequence of continuous motion capture data. First, an index function, based on posture features of each motion frame, is introduced to segment the given motion capture data into indexed motion clips. Then, based on the fact that motion coherence implies index coherence, a new motion with start frame f/sub start/ and end frame f/sub end/ can be synthesized by finding a smooth path connecting f/sub start/ to f/sub end/ in the multidimensional index space. Moreover, an algorithm for finding multiple smooth paths is presented. The merit of proposed framework is that it can generate fast prototyping of desired motions with only a small amount of preprocessing time. Experimental results are given to show the effectiveness of the proposed framework.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132014558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic X-ray image segmentation for threat detection","authors":"Jimin Liang, B. Abidi, M. Abidi","doi":"10.1109/ICCIMA.2003.1238158","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238158","url":null,"abstract":"Multithresholding and data clustering techniques are used to segment X-ray images for low intensity threat detection in carry-on luggage. The widely used statistical validity indexes methods do not generate a reasonable estimation of the optimal number of clusters and produce a biased evaluation of the segmented images acquired by different segmentation methods. We propose a method based on the Radon transform to determine the optimal number of clusters and to evaluate the segmented images. The method utilizes both statistical and spatial information from the image and is computationally efficient. Experimental results show that the proposed method produces results consistent with human visual assessment.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115895589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Immunity clone strategy based ICA","authors":"Fang Liu, Fan Jia","doi":"10.1109/ICCIMA.2003.1238141","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238141","url":null,"abstract":"In this paper, immunity clone algorithm is introduced to the learning of separating matrix in independent component analysis (ICA). For no restriction on the derivative of objective function, we can derive the matrix without help of traditional gradient descend algorithms. An objective function with a smaller error to approximate the negentropy is adopted. This algorithm has the advantages of simpleness, stable and global convergence, and is verified with computer simulation.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"28 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120813287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ISPEA: improvement for the strength Pareto evolutionary algorithm for multiobjective optimization with immunity","authors":"Meng Hongyun, L. Sanyang","doi":"10.1109/ICCIMA.2003.1238153","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238153","url":null,"abstract":"Recently, there arose some important multiobjective evolutionary algorithms (MOEAs), among these MOEAs, strength Pareto evolutionary algorithm (SPEA) seems the most effective technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems with several characteristics. Unfortunately, there are always some basic and obvious characteristics or knowledge in pending problem, where the loss due to this negligence is sometimes considerable in dealing with complex problems. Based on these reasons, an improvement on SPEA with immunity is given to restrain degeneracy of the evolution process, where the immune operator is realized by vaccine extraction, vaccination and immune selection in turn. Simulations show the ISPEA is effective and feasible.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121719790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A novel approach in off-line handwritten Chinese character stroke segmentation","authors":"Tao Ban, Chang-shui Zhang, Wei Shu, Zhongbao Kou","doi":"10.1109/ICCIMA.2003.1238144","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238144","url":null,"abstract":"In recognition of hand-written characters, stroke segmentation often serves as a crucial step. In this paper, we introduce a new method called manifold extraction to solve this problem. The basic idea of manifold extraction is: first build a neighborhood graph to capture the intrinsic topological structure of the sampled characters, then analyze the dimensional uniformity of neighboring points to discover the segments of strokes, finally combine the segments that are possibly from the same stroke and get the more informative structures of the characters. In this way, manifold extraction identifies the interlacing strokes in a complicated background and accomplishes the step of stroke segmentation. The experimental results show the effectiveness of this method in stroke segmentation as well as in exploratory data analysis.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115236376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Noise characteristics based postprocessing method in watermarking detection","authors":"Zhong Hua, Jiao Li-cheng","doi":"10.1109/ICCIMA.2003.1238167","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238167","url":null,"abstract":"This paper proposed a postprocessing method to improve watermark correlating detection based on analysis of the characteristics of image distortion noise. The extracted watermark is classified and processed, separately, according to the characteristics of distortion noise. Analysis shows that the noise power can be reduced effectively. With the probability of false alarm kept unchanged, the probability of watermark detection can be increased. Experimental results demonstrate the effectiveness of the proposed method under different image distortions.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122634574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improvement to progressive fine granularity scalable video coding","authors":"Ding Gui-guang, Guo Bao-long","doi":"10.1109/ICCIMA.2003.1238133","DOIUrl":"https://doi.org/10.1109/ICCIMA.2003.1238133","url":null,"abstract":"In this paper, an improved scheme to PFGS is presented, namely, the improved progressive fine granularity scalable video coding (IPFGS in short). The proposed coding scheme can not only provide improved coding efficiency compared with PFGS but also prevent the error accumulation. In the IPFGS scheme, to improve the coding efficiency, a high-quality reference frame was used for all frames. To avoid the error propagation due to packet loss, the attenuation factors are introduced. When the attenuation factors are set special values, IPFGS scheme can become PFGS scheme. Our experimental results show the IPFGS scheme can improve video quality up to 0.5 dB over the PFGS scheme in average PSNR.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125388571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}