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2010 International Conference of Soft Computing and Pattern Recognition最新文献

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Classifiers based on rough mereology in analysis of DNA microarray data 基于粗糙流变学的DNA微阵列数据分析分类器
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686493
Piotr Artiemjew
{"title":"Classifiers based on rough mereology in analysis of DNA microarray data","authors":"Piotr Artiemjew","doi":"10.1109/SOCPAR.2010.5686493","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686493","url":null,"abstract":"There are numerous application of DNA microar-rays. The most frequently used application is the one used to research gene expression. The aim is, among others, to detect symptoms of illnesses in tissues, to predict the predisposition for some illnesses and for personal identification. The huge amount of information obtained from DNA microarrays in range of tens of thousands of genes causes many difficulties. Many algorithms, for instance, many rough set methods cannot be applied for this reason and due to low number of training objects tend to overfit. In this paper, we aim at presenting gene separation and classification methods by means of the granular classifier based on weighted voting, which was investigated recently by Polkowski and Artiemjew. Results of the research show that the obtained results are in many cases better than some standard methods such as rough set standard exhaustive classifier and k-nearest neighbor.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117000271","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}
引用次数: 6
Background subtraction for object detection under varying environments 背景减法在不同环境下的目标检测
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685960
Saeed Vahabi Mashak, Behnam Hosseini, M. Mokji, S. Abu-Bakar
{"title":"Background subtraction for object detection under varying environments","authors":"Saeed Vahabi Mashak, Behnam Hosseini, M. Mokji, S. Abu-Bakar","doi":"10.1109/SOCPAR.2010.5685960","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685960","url":null,"abstract":"Background subtraction is widely used for extracting unusual motion of object of interest in video images. In this paper, we propose a fast and flexible approach of object detection based on an adaptive background subtraction technique that also effectively eliminates shadows based on color constancy principle in RGB color space. This approach can be used for both outdoor and indoor environments. Our proposed method of background subtraction makes use of multiple thresholding technique for detecting object of interests for any given scene. Once the moving object has been detected from the complex background, then the shadows are detected and eliminated by considering some environmental parameters.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122919916","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}
引用次数: 11
Particle swarm optimization based fixed order controller design and system simulation 基于粒子群优化的定序控制器设计与系统仿真
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685970
Faizullah Mahar, S. Ali
{"title":"Particle swarm optimization based fixed order controller design and system simulation","authors":"Faizullah Mahar, S. Ali","doi":"10.1109/SOCPAR.2010.5685970","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685970","url":null,"abstract":"Design of fixed order controllers is an open problem which takes controller complexity into account. Different solutions exist in literature for the fixed order controller design problem. However, major difficulty in these results is that they are not simple to develop an algorithm that guarantees the decisive solutions coupled with computations. To overcome this difficulty H∞ loop shaping control under an order specified controller has been proposed. The performance specification is formulated as cost function, which is to be minimized. Particle swarm optimization has been adopted for optimization of cost function and parameters of the specified controller. To check the success of proposed techniques, resulting controller parameters are evaluated via system simulations. Simulation results demonstrate the efficiency of proposed technique.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122460858","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}
引用次数: 2
Ultra fast fingerprint indexing for embedded system 用于嵌入式系统的超快速指纹索引
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686406
Ruili Zhou, SangWoo Sin, Dongju Li, T. Isshiki, H. Kunieda
{"title":"Ultra fast fingerprint indexing for embedded system","authors":"Ruili Zhou, SangWoo Sin, Dongju Li, T. Isshiki, H. Kunieda","doi":"10.1109/SOCPAR.2010.5686406","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686406","url":null,"abstract":"A novel fingerprint indexing scheme for embedded system is presented in this paper. Our approach is a modelbased one, which efficiently retrieves correct hypotheses using novel rotation-invariant features formed by the core and minutiae of fingerprint image. Differently from most existing fingerprint indexing approaches, the proposed algorithm is suitable for embedded systems because it is not time consuming and does not acquire large memory. Experiments were conducted on the FVC2002 database and our private database respectively. Experiment results based on FVC2002 database show that the time consuming for identification using proposed algorithm is about 50 times faster than the conventional approaches.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036755","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}
引用次数: 1
Damageless image hashing using neural network 基于神经网络的无损图像哈希
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686508
Kensuke Naoe, Yoshiyasu Takefuji
{"title":"Damageless image hashing using neural network","authors":"Kensuke Naoe, Yoshiyasu Takefuji","doi":"10.1109/SOCPAR.2010.5686508","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686508","url":null,"abstract":"In this paper, we present a new key generation model for image hashing using neural network, which does not embed any data into the content but is able to extract meaningful data from target image. This model trains artificial neural network to assign predefined code and uses this trained artificial neural network weight and the coordinates of the selected feature sub blocks of target image as keys to extract the predefined code. In this model, the observed output signal from the trained neural network is used as image hash value which distinguishes the target image from other images. The proposed method contributes to secure image hashing for content identification without damaging or losing any detailed data of visual images. The proposed method realizes an application for image authentication, image similarity comparison, verification of image integrity and copyright protection of multimedia contents.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"56 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113960868","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}
引用次数: 5
Mining web videos for video quality assessment 挖掘网络视频进行视频质量评估
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686400
D. Culibrk, Milan Mirković, P. Lugonja, V. Crnojevic
{"title":"Mining web videos for video quality assessment","authors":"D. Culibrk, Milan Mirković, P. Lugonja, V. Crnojevic","doi":"10.1109/SOCPAR.2010.5686400","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686400","url":null,"abstract":"Correlating estimates of objective measures related to the presence of different coding artifacts with the quality of video as perceived by human observers is a non-trivial task. There is no shortage of data to learn from, thanks to the Internet and web-sites such as YouTubetm. There has, however, been little done in the research community to try to use such resources to advance our understanding of perceived video quality. The problem is the fact that it is not easy to obtain the Mean Opinion Score (MOS), a standard measure of the perceived video quality, for more than a handful of videos. The paper presents an approach to determining the quality of a relatively large number of videos obtained randomly from YouTubetm. Several measures related to motion, saliency and coding artifacts are calculated for the frames of the video. Programmable graphics hardware is used to perform clustering: first, to create an artifacts-related signature of each video; then, to cluster the videos according to their signatures. To obtain an estimate for the video quality, MOS is obtained for representative videos, closest to the cluster centers. This is then used as an estimate of the quality of all other videos in the cluster. Results based on 2,107 videos containing some 90,000,000 frames are presented in the paper.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389700","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}
引用次数: 6
Speech recognition by indexing and sequencing 语音识别的索引和排序
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686409
Simone Franzini, J. Ben-Arie
{"title":"Speech recognition by indexing and sequencing","authors":"Simone Franzini, J. Ben-Arie","doi":"10.1109/SOCPAR.2010.5686409","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686409","url":null,"abstract":"Recognition by Indexing and Sequencing (RISq) is a general-purpose method for classification of temporal vector sequences. We developed an advanced version of RISq and applied it to isolated-word speech recognition, a task most commonly performed with Hidden Markov Models (HMMs) or Dynamic Time Warping (DTW). RISq is substantially different from both these methods and presents several advantages over them: robust recognition can be achieved with only a few samples from the input sequence and training can be carried out with one or more examples per class. This enables much faster training and also allows to recognize speech with a variety of accents. A two-step classification algorithm is used: first the training samples closest to each input sample are identified and weighted with a parallel algorithm (indexing). Then a maximum weighted bipartite graph matching is found between the input sequence and a training sequence, respecting an additional temporal constraint (sequencing). We discuss the application of RISq to speech recognition and compare its architecture and performance with that of Sphinx, a state-of-the-art speech recognizer based on HMMs.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127501898","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}
引用次数: 10
Harmony search algorithm for simultaneous clustering and feature selection 和谐搜索算法的同时聚类和特征选择
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686097
H. Sarvari, Nima Khairdoost, Abdolvahhab Fetanat
{"title":"Harmony search algorithm for simultaneous clustering and feature selection","authors":"H. Sarvari, Nima Khairdoost, Abdolvahhab Fetanat","doi":"10.1109/SOCPAR.2010.5686097","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686097","url":null,"abstract":"Feature selection is an important process in data analysis for information-preserving data reduction. Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper, we propose an approach for clustering and feature selection simultaneously using a harmony search algorithm. Our approach makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both feature selection and clustering, without making any a prior assumption about the number of clusters. Within ourmethod, a variable composite representation is devised toencode both feature selection and cluster centers with different numbers of clusters. Furthermore, local search operations are used to improve feature selection and cluster centers encoded in the harmonics.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117127232","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}
引用次数: 10
Describing acceptable objects by means of Sugeno integrals 用Sugeno积分描述可接受对象
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685870
H. Prade, A. Rico
{"title":"Describing acceptable objects by means of Sugeno integrals","authors":"H. Prade, A. Rico","doi":"10.1109/SOCPAR.2010.5685870","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685870","url":null,"abstract":"Objects are usually described by combinations of properties. Logic-based descriptions offer compact representations for binary properties. Besides, Sugeno integrals are well-known as a powerful qualitative aggregation tool in multiple-criteria decision, which is applicable to gradual properties, and takes into account positive synergies between properties. The paper proposes to investigate the potential use of Sugeno integrals as a representation tool, to lay bare their relation with possibilistic logic representations, and to discuss the handling of negative synergies in this setting using a pair of Sugeno integrals.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134510605","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}
引用次数: 4
Design optimization of analog integrated circuits by using artificial neural networks 基于人工神经网络的模拟集成电路设计优化
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686736
A. Jafari, S. Sadri, M. Zekri
{"title":"Design optimization of analog integrated circuits by using artificial neural networks","authors":"A. Jafari, S. Sadri, M. Zekri","doi":"10.1109/SOCPAR.2010.5686736","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686736","url":null,"abstract":"This paper presents a computer-aided design (CAD) tool for automated sizing and optimization of analog integrated circuits (ICs). This tool uses artificial neural networks (ANNs) in order to deduce the device sizes that optimize the performance objectives while satisfying the constraint specifications. Neural networks can learn and generalize from data allowing model development even when component formulas are unavailable. The training data are obtained by various simulations in the HSPICE design environment with TSMC 0.18 μm CMOS process parameters. To evaluate the tool, one practical example is presented in 0.18 μm CMOS technology. The simulation results verify effectiveness of the proposed method for analog circuits sizing.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134157638","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}
引用次数: 28
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