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2009 Seventh International Conference on Advances in Pattern Recognition最新文献

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Speeding up AdaBoost Classifier with Random Projection 用随机投影加速AdaBoost分类器
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.67
Biswajit Paul, G. Athithan, M. Murty
{"title":"Speeding up AdaBoost Classifier with Random Projection","authors":"Biswajit Paul, G. Athithan, M. Murty","doi":"10.1109/ICAPR.2009.67","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.67","url":null,"abstract":"The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130282561","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: 18
An Efficient Bilingual Optical Character Recognition (English-Oriya) System for Printed Documents 一种高效的印刷文件双语光学字符识别系统
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.49
S. Mohanty, Himadri Nandini Dasbebartta, Tarun Kumar Behera
{"title":"An Efficient Bilingual Optical Character Recognition (English-Oriya) System for Printed Documents","authors":"S. Mohanty, Himadri Nandini Dasbebartta, Tarun Kumar Behera","doi":"10.1109/ICAPR.2009.49","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.49","url":null,"abstract":"Recognition of documents containing multiscripts is really a challenging task, which needs more effort of the OCR (Optical Character Recognition) designers for improving the accuracy rate. Previously OCR was developed for documents with single scripts only mainly for English and regional languages. Old documents of not only uniscripts but also multiscripts is needed to be preserved for future use. This paper describes the character recognition process for printed documents containing English and Oriya texts. Though the languages in India are different but still we can find some common features among them. In consideration to our paper we need to distinguish between the Roman Script and the Oriya Script. Most of the English that is. Roman Script are linear as well as circular in nature and the Oriya characters are circular in nature. So we need to separate these scripts by taking into consideration of their features paragraph wise or line wise.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480622","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: 22
Spread Spectrum Watermark Embedder Optimization Using Genetic Algorithms 基于遗传算法的扩频水印嵌入优化
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.85
S. Maity, S. Maity, J. Sil
{"title":"Spread Spectrum Watermark Embedder Optimization Using Genetic Algorithms","authors":"S. Maity, S. Maity, J. Sil","doi":"10.1109/ICAPR.2009.85","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.85","url":null,"abstract":"This paper looks Spread Spectrum (SS) watermarking from a different angle where number of cover signal points, payload capacity and watermark signal-to-interference ratio are optimized. The objective is to meet an acceptable BER (bit error rate) and peak-to-average distortion (PAD)on a single point of the cover signal under the constraint of a given embedding distortion and cover size. First, a new model of spread spectrum (SS) watermarking is proposed where each watermark bit is spread using a distinct code pattern over N-mutually orthogonal signal points. Decision variable for each bit of watermark decoding is formed from the weighted average of N-decision statistics. Each watermarked signal point is then modified (attack channel) as Rayleigh distribution followed by AWGN (additive white Gaussian noise).  Genetic algorithm (GA) isused to reduce the searching time in this multidimensional nonlinear problem of conflicting nature. Simulation results show that optimizing the number of cover signal  points, payload capacity and watermark signal-to-interference  ratio (WSIR), better acceptable values of both BER and PAD can be  achieved simultaneously.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360910","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
A Validity Index Based on Connectivity 基于连通性的有效性索引
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.53
S. Saha, S. Bandyopadhyay
{"title":"A Validity Index Based on Connectivity","authors":"S. Saha, S. Bandyopadhyay","doi":"10.1109/ICAPR.2009.53","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.53","url":null,"abstract":"In this paper we have developed a connectivity based cluster validity index. This validity index is able to detect the number of clusters automatically from data sets having well separated clusters of any shape, size or convexity. The proposed cluster validity index, connect-index, uses the concept of relative neighborhood graph for measuring the amount of \"connectedness\" of a particular cluster. The proposed connect-index is inspired by the popular Dunn's index for measuring the cluster validity. Single linkage clustering algorithm is used as the underlying partitioning technique. The superiority of the proposed validity measure in comparison with Dunn's index is shown for four artificial and two real-life data sets.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126975150","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: 13
SVM Based Shot Boundary Detection Using Block Motion Feature Based on Statistical Moments 基于统计矩的块运动特征的SVM镜头边界检测
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.25
B. Bhowmick, Kaustav Goswami
{"title":"SVM Based Shot Boundary Detection Using Block Motion Feature Based on Statistical Moments","authors":"B. Bhowmick, Kaustav Goswami","doi":"10.1109/ICAPR.2009.25","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.25","url":null,"abstract":"Temporal video segmentation is of fundamental importance in order to facilitate user’s access to huge volume of video data as well as for video summarization.The objective of shot boundary detection is to partition the video into meaningful, basic structural units called shots. In this paper, a shot boundary detection technique has been proposed for cuts. The method extracts block feature based similarities from the frames of the input video. Statistical moments up to second order are used to measure the motion present in the frames. Feature vectors are generated using a sliding window over time and are trained by a SVM to identify the cuts.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127685216","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
Combining Features for Shape and Motion Trajectory of Video Objects for Efficient Content Based Video Retrieval 结合视频对象的形状和运动轨迹特征，实现高效的基于内容的视频检索
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.37
A. Dyana, M. P. Subramanian, Sukhendu Das
{"title":"Combining Features for Shape and Motion Trajectory of Video Objects for Efficient Content Based Video Retrieval","authors":"A. Dyana, M. P. Subramanian, Sukhendu Das","doi":"10.1109/ICAPR.2009.37","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.37","url":null,"abstract":"This paper proposes a system for content based video retrieval based on shape and motion features of the video object. We have used Curvature scale space for shape representation and Polynomial curve fitting for trajectory representation and retrieval. The shape representation is invariant to translation, rotation and scaling and robust with respect to noise. Trajectory matching incorporates visual distance, velocity dissimilarity and size dissimilarity for retrieval. The cost of matching two video objects is based on shape and motion features, to retrieve similar video shots. We have tested our system on standard synthetic databases. We have also tested our system on real world databases. Experimental results have shown good performance.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114683510","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
Integration of Region and Edge-based information for Efficient Road Extraction from High Resolution Satellite Imagery 基于区域和边缘信息的高分辨率卫星图像道路提取方法
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.42
T. T. Mirnalinee, Sukhendu Das, K. Varghese
{"title":"Integration of Region and Edge-based information for Efficient Road Extraction from High Resolution Satellite Imagery","authors":"T. T. Mirnalinee, Sukhendu Das, K. Varghese","doi":"10.1109/ICAPR.2009.42","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.42","url":null,"abstract":"In Remote sensing systems one of the most important features needed are roads, which require automated procedures to rapidly identify them from high-resolution satellite imagery, Many approaches for automatic road extraction have appeared in literature [2][7][9], which vary due to the differences in their goals, available information, algorithms used and assumptions about roads. In this paper, we propose an approach for automatic road extraction by integrating region and edge information. The complimentary information of road segments obtained using Probabilistic SVM(PSVM) and road edges obtained using Dominant Singular Measure (DSM) are integrated using a modified Constraint Satisfaction Neural Network -Complementary Information Integration(CSNN-CII) [1] to improve the accuracy of the system. Results are shown on real-world images and quantitatively evaluated with manual hand-drawn road layouts.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121056869","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
Multiobjective Genetic Clustering with Ensemble Among Pareto Front Solutions: Application to MRI Brain Image Segmentation Pareto前解集成的多目标遗传聚类在MRI脑图像分割中的应用
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.51
A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay
{"title":"Multiobjective Genetic Clustering with Ensemble Among Pareto Front Solutions: Application to MRI Brain Image Segmentation","authors":"A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay","doi":"10.1109/ICAPR.2009.51","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.51","url":null,"abstract":"This article describes a multiobjective genetic fuzzy clustering scheme that utilizes the search capabilities of NSGA-II, a popular multiobjective genetic algorithm and optimizes a number of fuzzy cluster validity measures. Real-coded encoding of the cluster centers is used for this purpose. The multiobjective clustering scheme produces a number of non-dominated solutions, each of which contains some information about the clustering structure. Hence it is required to obtain the final optimal clustering by combining those information. For this, clustering ensemble is used to combine the non-dominated solutions of the final Pareto front produced. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density-weighted normal MRI brain images. Superiority of the proposed method over K-means, Fuzzy C-means, Expectation Maximization and Single Objective Genetic clustering have been demonstrated.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127639674","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: 47
Online Improved Eigen Tracking 在线改进特征跟踪
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.39
Subarna Tripathi, S. Chaudhury, Sumantra Dutta Roy
{"title":"Online Improved Eigen Tracking","authors":"Subarna Tripathi, S. Chaudhury, Sumantra Dutta Roy","doi":"10.1109/ICAPR.2009.39","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.39","url":null,"abstract":"We present a novel predictive statistical framework to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the object being tracked on the fly using candid co-variance free incremental PCA. The proposed system detects and tracks an object in the scene by learning the appearance model of the object online motivated by non-traditional uniform norm. It speeds up the tracker many fold by avoiding nonlinear optimization generally used in the literature.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679109","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: 3
Bengali Named Entity Recognition Using Classifier Combination 基于分类器组合的孟加拉语命名实体识别
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-01 DOI: 10.1109/ICAPR.2009.86
Asif Ekbal, Sivaji Bandyopadhyay
{"title":"Bengali Named Entity Recognition Using Classifier Combination","authors":"Asif Ekbal, Sivaji Bandyopadhyay","doi":"10.1109/ICAPR.2009.86","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.86","url":null,"abstract":"This paper reports about the development of a Named Entity Recognition (NER) system for Bengali by combining the outputs of the classifiers like Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine(SVM) using a majority voting approach. The training set consists of approximately 150K word forms and has been manually annotated with the four major NE tags such as Person name, Location name, Organization name and Miscellaneous name tags. Lexical context patterns, generated from an unlabeled corpus of 3 million word forms, have been used in order to improve the performance of the classifiers.Evaluation results of the voted system for the gold standard test set of 30K word forms have demonstrated the overall recall, precision, and f-Score values of 87.11%, 83.61%, and 85.32%, respectively, which shows an improvement of 4.66%in f-Score over the best performing SVM based system and an improvement of 9.5% in f-score over the least performing ME based system.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"2336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365392","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: 23
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