Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208282
A. Banumathi, A. Pethalakshmi
Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it's difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.
聚类是一种广泛应用于数据挖掘的技术，用于在大数据集中发现模式。本文对模糊c均值算法进行了分析，发现聚类结果的质量取决于初始种子，初始种子的选择可以是顺序的，也可以是随机的。模糊C-Means采用K-Means聚类方法进行聚类的初始操作，然后计算隶属度。模糊C-Means与K-Means算法非常相似，因此本文概述了K-Means算法，并证明了如何通过UCAM (Unique Clustering with Affinity Measure)聚类算法纠正K-Means算法的缺点，然后对UCAM进行改进，给出了一种新的观点，即Fuzzy-UCAM。模糊C-均值算法的初始化需要有聚类C的个数和初始种子的个数。对于实时的大型数据库，很难准确地预测聚类和初始种子的数量。为了克服这一缺点，本文重点研究了不给出模糊c均值初始种子和簇数的Fuzzy- ucam聚类算法。利用亲和度量获得了唯一的聚类。
{"title":"Increasing cluster uniqueness in Fuzzy C-Means through affinity measure","authors":"A. Banumathi, A. Pethalakshmi","doi":"10.1109/ICPRIME.2012.6208282","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208282","url":null,"abstract":"Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it's difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"341 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955953","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208342
C. L. Chowdhary, P. Mouli
In present scenario, it is challenging to access widely distributed and huge data from many network systems to a single network system. There are several problems like, monitoring of remote devices and controlling of its operations. A reliable, secure and platform-free remote controller, with ability of monitoring, can overcome such problems. In this paper, a new design of network-based remote controlling and monitoring system is proposed which is platform-free and more secure in comparison with other existing systems. The basic concept is to use the network base for the purpose of real-time remote monitoring and controlling of processing equipment.
{"title":"Design and implementation of secure, platform-free, and network-based remote controlling and monitoring system","authors":"C. L. Chowdhary, P. Mouli","doi":"10.1109/ICPRIME.2012.6208342","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208342","url":null,"abstract":"In present scenario, it is challenging to access widely distributed and huge data from many network systems to a single network system. There are several problems like, monitoring of remote devices and controlling of its operations. A reliable, secure and platform-free remote controller, with ability of monitoring, can overcome such problems. In this paper, a new design of network-based remote controlling and monitoring system is proposed which is platform-free and more secure in comparison with other existing systems. The basic concept is to use the network base for the purpose of real-time remote monitoring and controlling of processing equipment.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121140440","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208291
E. K. Nesamalar, C. P. Chandran
In this paper Flexible Protein Ligand Docking is carried out using Genetic Clustering with Bee Colony Optimization. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. A Genetic clustering algorithm combine a Genetic Algorithm (GA) with the K-medians clustering algorithm. GA is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a new Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, we propose a new algorithm called Genetic clustering Bee Colony Optimization (GCBCO). The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.
{"title":"Genetic clustering with Bee Colony Optimization for flexible protein-ligand docking","authors":"E. K. Nesamalar, C. P. Chandran","doi":"10.1109/ICPRIME.2012.6208291","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208291","url":null,"abstract":"In this paper Flexible Protein Ligand Docking is carried out using Genetic Clustering with Bee Colony Optimization. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. A Genetic clustering algorithm combine a Genetic Algorithm (GA) with the K-medians clustering algorithm. GA is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a new Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, we propose a new algorithm called Genetic clustering Bee Colony Optimization (GCBCO). The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893305","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208296
K. R. Nanthagobal, C. Chandrasekar
The service discovery mechanism in next generation wireless network should be flexible to both location and environment change of the user which can be achieved by appropriately predicting the user mobility. As a result, effective user mobility prediction technique need to be designed for offering the services without affecting the user location. In this paper, we propose a location aware service discovery protocol in next generation wireless networks. This technique consists of three phases: Handoff triggering based on received signal strength of the base station (BS), Client mobility prediction as per its velocity and direction, BS selection with maximum available bandwidth and residual power. By simulation results, we show that our proposed approach minimizes the query latency.
{"title":"Location-aware service discovery in next generation wireless networks","authors":"K. R. Nanthagobal, C. Chandrasekar","doi":"10.1109/ICPRIME.2012.6208296","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208296","url":null,"abstract":"The service discovery mechanism in next generation wireless network should be flexible to both location and environment change of the user which can be achieved by appropriately predicting the user mobility. As a result, effective user mobility prediction technique need to be designed for offering the services without affecting the user location. In this paper, we propose a location aware service discovery protocol in next generation wireless networks. This technique consists of three phases: Handoff triggering based on received signal strength of the base station (BS), Client mobility prediction as per its velocity and direction, BS selection with maximum available bandwidth and residual power. By simulation results, we show that our proposed approach minimizes the query latency.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130107520","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208351
M. Sundaresan, E. Devika
Compound image is combination of text, graphics and pictures. Compression is the process of reducing the amount of data required to represent information. It also reduces the time required for the data to be sent over the Internet or Web pages. Compound image compression is done on the basis of lossy and lossless compression. Lossy compression is a data encoding method that compresses data by discarding (losing) some data in the image. Lossless compression is used to compress the image without any loss of data in the image. Image compression is done using lossy compression and lossless compression. In this paper different techniques are used for compressing compound images. The performance of these techniques has been compared.
{"title":"Image compression using H.264 and deflate algorithm","authors":"M. Sundaresan, E. Devika","doi":"10.1109/ICPRIME.2012.6208351","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208351","url":null,"abstract":"Compound image is combination of text, graphics and pictures. Compression is the process of reducing the amount of data required to represent information. It also reduces the time required for the data to be sent over the Internet or Web pages. Compound image compression is done on the basis of lossy and lossless compression. Lossy compression is a data encoding method that compresses data by discarding (losing) some data in the image. Lossless compression is used to compress the image without any loss of data in the image. Image compression is done using lossy compression and lossless compression. In this paper different techniques are used for compressing compound images. The performance of these techniques has been compared.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132572","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208360
R. Boss, K. Thangavel, D. Daniel
This paper proposes mammogram image segmentation using Fuzzy C-Means (FCM) clustering algorithm. The median filter is used for pre-processing of image. It is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means and FCM algorithms inorder to segment the region of interests for further classification. The performance of segmentation result of the proposed algorithm is measured according to the error values such as Mean Square Error (MSE) and Root Means Square Error (RMSE). The Mammogram images used in our experiment are obtained from MIAS database.
本文提出了一种基于模糊c均值(FCM)聚类算法的乳房x线图像分割方法。采用中值滤波器对图像进行预处理。它通常用于减少图像中的噪声。利用灰度共生矩阵(GLCM)对不同角度的乳房x线照片提取14个哈拉利克特征。通过K-Means和FCM算法对特征进行聚类，以分割感兴趣的区域进行进一步分类。根据均方误差(Mean Square error, MSE)和均方根误差(Root Mean Square error, RMSE)等误差值来衡量该算法的分割效果。在我们的实验中使用的乳房x光图像是从MIAS数据库中获得的。
{"title":"Mammogram image segmentation using fuzzy clustering","authors":"R. Boss, K. Thangavel, D. Daniel","doi":"10.1109/ICPRIME.2012.6208360","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208360","url":null,"abstract":"This paper proposes mammogram image segmentation using Fuzzy C-Means (FCM) clustering algorithm. The median filter is used for pre-processing of image. It is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means and FCM algorithms inorder to segment the region of interests for further classification. The performance of segmentation result of the proposed algorithm is measured according to the error values such as Mean Square Error (MSE) and Root Means Square Error (RMSE). The Mammogram images used in our experiment are obtained from MIAS database.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131240500","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208344
P. Prasenna, A. V. T. RaghavRamana, R. Krishnakumar, A. Devanbu
In conventional network security simply relies on mathematical algorithms and low counter measures to taken to prevent intrusion detection system, although most of this approaches in terms of theoretically challenged to implement. Therefore, a variety of algorithms have been committed to this challenge. Instead of generating large number of rules the evolution optimization techniques like Genetic Network Programming (GNP) can be used. The GNP is based on directed graph, In this paper the security issues related to deploy a data mining-based IDS in a real time environment is focused upon. We generalize the problem of GNP with association rule mining and propose a fuzzy weighted association rule mining with GNP framework suitable for both continuous and discrete attributes. Our proposal follows an Apriori algorithm based fuzzy WAR and GNP and avoids pre and post processing thus eliminating the extra steps during rules generation. This method can sufficient to evaluate misuse and anomaly detection. Experiments on KDD99Cup and DARPA98 data show the high detection rate and accuracy compared with other conventional method.
{"title":"Network programming and mining classifier for intrusion detection using probability classification","authors":"P. Prasenna, A. V. T. RaghavRamana, R. Krishnakumar, A. Devanbu","doi":"10.1109/ICPRIME.2012.6208344","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208344","url":null,"abstract":"In conventional network security simply relies on mathematical algorithms and low counter measures to taken to prevent intrusion detection system, although most of this approaches in terms of theoretically challenged to implement. Therefore, a variety of algorithms have been committed to this challenge. Instead of generating large number of rules the evolution optimization techniques like Genetic Network Programming (GNP) can be used. The GNP is based on directed graph, In this paper the security issues related to deploy a data mining-based IDS in a real time environment is focused upon. We generalize the problem of GNP with association rule mining and propose a fuzzy weighted association rule mining with GNP framework suitable for both continuous and discrete attributes. Our proposal follows an Apriori algorithm based fuzzy WAR and GNP and avoids pre and post processing thus eliminating the extra steps during rules generation. This method can sufficient to evaluate misuse and anomaly detection. Experiments on KDD99Cup and DARPA98 data show the high detection rate and accuracy compared with other conventional method.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122624894","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208390
M. M. Ali, L. Rajamani
Deceptive Phishing is the major problem in Instant Messengers, much of sensitive and personal information, disclosed through socio-engineered text messages for which solution is proposed[2] but, detection of phishing through voice chatting technique in Instant Messengers is not yet done which is the motivating factor to carry out the work and solution to address this problem of privacy in Instant Messengers (IM) is proposed using Association Rule Mining (ARM) technique a Data Mining approach integrated with Speech Recognition system. Words are recognized from speech with the help of FFT spectrum analysis and LPC coefficients methodologies. Online criminal's now-a-days adapted voice chatting technique along with text messages collaboratively or either of them in IM's and wraps out personal information leads to threat and hindrance for privacy. In order to focus on privacy preserving we developed and experimented Anti Phishing Detection system (APD) in IM's to detect deceptive phishing for text and audio collaboratively.
{"title":"Deceptive phishing detection system: From audio and text messages in Instant Messengers using Data Mining approach","authors":"M. M. Ali, L. Rajamani","doi":"10.1109/ICPRIME.2012.6208390","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208390","url":null,"abstract":"Deceptive Phishing is the major problem in Instant Messengers, much of sensitive and personal information, disclosed through socio-engineered text messages for which solution is proposed[2] but, detection of phishing through voice chatting technique in Instant Messengers is not yet done which is the motivating factor to carry out the work and solution to address this problem of privacy in Instant Messengers (IM) is proposed using Association Rule Mining (ARM) technique a Data Mining approach integrated with Speech Recognition system. Words are recognized from speech with the help of FFT spectrum analysis and LPC coefficients methodologies. Online criminal's now-a-days adapted voice chatting technique along with text messages collaboratively or either of them in IM's and wraps out personal information leads to threat and hindrance for privacy. In order to focus on privacy preserving we developed and experimented Anti Phishing Detection system (APD) in IM's to detect deceptive phishing for text and audio collaboratively.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127870962","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208391
S. C. Sajjan, C. Vijaya
This study proposes limited vocabulary isolated word recognition using Linear Predictive Coding(LPC) and Mel Frequency Cepstral Coefficients(MFCC) for feature extraction, Dynamic Time Warping(DTW) and discrete Hidden Markov Model (HMM) for recognition and their comparisons. Feature extraction is carried over the speech frame of 300 samples with 100 samples overlap at 8 KHz sampling rate of the input speech. MFCC analysis provides better recognition rate than LPC as it operates on a logarithmic scale which resembles human auditory system whereas LPC has uniform resolution over the frequency plane. This is followed by pattern recognition. Since the voice signal tends to have different temporal rate, DTW is one of the methods that provide non-linear alignment between two voice signals. Another method called HMM that statistically models the words is also presented. Experimentally it is observed that recognition accuracy is better for HMM compared with DTW. The database used is TI-46 isolated word corpus zero-nine from Linguist Data Consortium.
本研究提出了使用线性预测编码(LPC)和Mel频率倒谱系数(MFCC)进行特征提取，动态时间扭曲(DTW)和离散隐马尔可夫模型(HMM)进行识别并比较有限词汇孤立词的方法。在输入语音的8 KHz采样率下，对300个样本的语音帧进行特征提取，其中100个样本重叠。MFCC分析具有比LPC更好的识别率，因为它在类似于人类听觉系统的对数尺度上运行，而LPC在频率平面上具有均匀的分辨率。接下来是模式识别。由于语音信号往往具有不同的时间速率，DTW是在两个语音信号之间提供非线性对准的方法之一。本文还介绍了另一种称为HMM的方法，该方法可以对单词进行统计建模。实验结果表明，HMM的识别精度优于DTW。使用的数据库是来自Linguist Data Consortium的TI-46孤立词语料库zero- 9。
{"title":"Comparison of DTW and HMM for isolated word recognition","authors":"S. C. Sajjan, C. Vijaya","doi":"10.1109/ICPRIME.2012.6208391","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208391","url":null,"abstract":"This study proposes limited vocabulary isolated word recognition using Linear Predictive Coding(LPC) and Mel Frequency Cepstral Coefficients(MFCC) for feature extraction, Dynamic Time Warping(DTW) and discrete Hidden Markov Model (HMM) for recognition and their comparisons. Feature extraction is carried over the speech frame of 300 samples with 100 samples overlap at 8 KHz sampling rate of the input speech. MFCC analysis provides better recognition rate than LPC as it operates on a logarithmic scale which resembles human auditory system whereas LPC has uniform resolution over the frequency plane. This is followed by pattern recognition. Since the voice signal tends to have different temporal rate, DTW is one of the methods that provide non-linear alignment between two voice signals. Another method called HMM that statistically models the words is also presented. Experimentally it is observed that recognition accuracy is better for HMM compared with DTW. The database used is TI-46 isolated word corpus zero-nine from Linguist Data Consortium.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671487","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}
Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208353
J. Veerappan, G. Pitchammal
The main theme of this application is to provide an algorithm for grayscale and color image watermark to manage the attacks such as rotation, scaling and translation. In the existing watermarking algorithms, those exploited robust features are more or less related to the pixel position, so they cannot be more robust against the attacks. In order to solve this problem this application focus on certain parameters rather than the pixel position for watermarking. Two statistical features such as the histogram shape and the mean of Gaussian filtered low-frequency component of images are taken for this proposed application to make the watermarking algorithm robust to attacks and also interpolation technique is used to increase the number of bites to be needed.
{"title":"Interpolation based image watermarking resisting to geometrical attacks","authors":"J. Veerappan, G. Pitchammal","doi":"10.1109/ICPRIME.2012.6208353","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208353","url":null,"abstract":"The main theme of this application is to provide an algorithm for grayscale and color image watermark to manage the attacks such as rotation, scaling and translation. In the existing watermarking algorithms, those exploited robust features are more or less related to the pixel position, so they cannot be more robust against the attacks. In order to solve this problem this application focus on certain parameters rather than the pixel position for watermarking. Two statistical features such as the histogram shape and the mean of Gaussian filtered low-frequency component of images are taken for this proposed application to make the watermarking algorithm robust to attacks and also interpolation technique is used to increase the number of bites to be needed.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121909923","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}
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