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

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Single training sample Face recognition using fusion of Gabor responses 基于Gabor响应融合的单训练样本人脸识别
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686081
R. Ebrahimpour, M. Abadi, Masoom Nazari, A. Amiri, Mehdi Azizi, M. Rahat
{"title":"Single training sample Face recognition using fusion of Gabor responses","authors":"R. Ebrahimpour, M. Abadi, Masoom Nazari, A. Amiri, Mehdi Azizi, M. Rahat","doi":"10.1109/SOCPAR.2010.5686081","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686081","url":null,"abstract":"This paper deals with Single training sample Face recognition which is a new challenging problem in pattern recognition and machine vision. In the proposed model, Gabor filter is applied on face images, to generate four Gabor responses. And then, the generated samples along with the original image used as the input of five Nearest Neighbor Base-classifiers. To increase the correct recognition rate, the votes of Base-Classifiers are combined using majority voting technique. The correct class of each test sample is determined by the majority of the classifiers votes. But in cases which the winner class can't be determined using majority voting (deep uncertainty), we use Enhanced Majority voting which chooses the vote of original image's Base-Classifier as the winner class. The resultant recognition rate of applying this model on ORL face database was improvement in recognition rate about 2%, 4% and 5% than 2DPCA, (PC)2 A and PCA respectively.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 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":"116438017","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
DNA microarray classification by means of weighted voting based on rough set classifier 基于粗糙集分类器的加权投票DNA微阵列分类
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686494
Przemyslaw Górecki, Piotr Artiemjew
{"title":"DNA microarray classification by means of weighted voting based on rough set classifier","authors":"Przemyslaw Górecki, Piotr Artiemjew","doi":"10.1109/SOCPAR.2010.5686494","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686494","url":null,"abstract":"In this paper we present a new approach for classification of microarray data. Our methodology consists of two steps: an attribute selection, which aims at selection of the most informative genes, and a classification of expression profiles, which is carried out by weighted voting, a novel instance-based classifier based on Rough Set Theory. Attribute selection consists of two stages — initial selection, where each attribute is evaluated individually, and attribute refinement, where the attributes are further reduced by means of genetic computations. The effectiveness of the proposed approach was verified on six different microarray datasets [13], and compared with attribute selection and classification based on nearest neighbor classifier.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 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":"129158434","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
Automatic image segmentation of greenness in crop fields 农田绿化图像的自动分割
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685936
María Isabel Riomoros, M. Guijarro, G. Pajares, P. Herrera, X. Burgos-Artizzu, A. Ribeiro
{"title":"Automatic image segmentation of greenness in crop fields","authors":"María Isabel Riomoros, M. Guijarro, G. Pajares, P. Herrera, X. Burgos-Artizzu, A. Ribeiro","doi":"10.1109/SOCPAR.2010.5685936","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685936","url":null,"abstract":"This paper describes a new automatic image segmentation strategy for segmenting green plants. The final goal is its application in Precision Agriculture. The goal is to identify several classes of greenness coming from the plants. We exploit the performance of several existing approaches so that conveniently combined allow us to design the automatic approach based on non automatic methods. First we apply a well known index-based strategy that accentuates the green spectral band from the remainder, giving a gray image. From the resulting image we apply the well-known thresholding Otsu's method obtaining a binary image, where the green part appears separated from the soil. Taking as input the green pixels we apply an unsupervised method and they are partitioned in a fixed number of classes. The performance of the method is tested against a set of available images and acquired in several crop fields of cereal and maize.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"27 2 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":"132554812","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: 12
The use of F-transform for image fusion algorithms 利用f变换进行图像融合的算法
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686496
I. Perfilieva, M. Daňková, P. Hodáková, M. Vajgl
{"title":"The use of F-transform for image fusion algorithms","authors":"I. Perfilieva, M. Daňková, P. Hodáková, M. Vajgl","doi":"10.1109/SOCPAR.2010.5686496","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686496","url":null,"abstract":"We show that on the basis of F-transform, the problem of reconstruction of corrupted images can be solved. The proposed technique is called image fusion. A simple linear algorithm of image fusion, based on F-transform, is proposed and justified. A measure of degradation of an image is proposed as well.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"554 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":"127866431","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: 9
Rough set and CART approaches to mining incomplete data 挖掘不完全数据的粗糙集和CART方法
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685860
J. Grzymala-Busse
{"title":"Rough set and CART approaches to mining incomplete data","authors":"J. Grzymala-Busse","doi":"10.1109/SOCPAR.2010.5685860","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685860","url":null,"abstract":"Many data sets are incomplete, i.e., are affected by missing attribute values. In this paper, we report results of experiments on two approaches to missing attribute values. The first one is based on rough set theory and rule induction, the second one is the CART method that uses surrogate splits for handling missing attribute values and that generates decision trees. As follows from our experiments, both approaches are comparable in terms of an error rate. Thus, for a specific data set the best method of handling missing attribute values should be selected individually.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"77 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":"121874988","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
Comparison of high capacity steganography techniques 高容量隐写技术的比较
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-01-13 DOI: 10.1109/SOCPAR.2010.5686507
Rosanne English
{"title":"Comparison of high capacity steganography techniques","authors":"Rosanne English","doi":"10.1109/SOCPAR.2010.5686507","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686507","url":null,"abstract":"The objective of this paper is to implement the Bit Plane Complexity Segmentation (BPCS) algorithm and provide a comparison in terms of effectiveness and hiding capacity with the least significant bits algorithm (LSB) using 4 bits. The BPCS algorithm was chosen due to the effective high hiding capacity promised. The four LSB algorithm was selected as it promised a similar capacity. The motivation for the work was to explicitly examine the hypothesis that the BPCS algorithm would out perform the four LSB algorithm using low order attacks. Effectiveness of the algorithms was measured by visual attacks and basic first order statistical attacks on the stego-images produced. The outcome of the research showed that whilst the hiding capacity of the two algorithms were comparable (both equating to roughly 50%), the four LSB algorithm at the maximum capacity was ineffective. This was due to the fact that a visual attack sufficed to realise there was something hidden. In contrast, the BPCS algorithm provided a much more effective method for obtaining a 50% capacity since visual attacks did not suffice for detection.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342198","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: 15
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