2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)最新文献_第2页

CGACLC: Improving genetic algorithm through clustering for designing of combinational logic circuits 基于聚类的改进遗传算法用于组合逻辑电路的设计

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983062

Zahra Alidousti, Mohammad Ehsan Basiri

引用次数: 0

Shrinkage estimator based common spatial pattern for multi-class motor imagery classification by hybrid classifier 基于收缩估计的混合分类器多类运动图像公共空间模式分类

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983059

Mohammadreza Edalati Sharbaf, A. Fallah, S. Rashidi

引用次数: 5

Sentence-level sentiment analysis in Persian 波斯语句子级情感分析

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983023

Mohammad Ehsan Basiri, Arman Kabiri

{"title":"Sentence-level sentiment analysis in Persian","authors":"Mohammad Ehsan Basiri, Arman Kabiri","doi":"10.1109/PRIA.2017.7983023","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983023","url":null,"abstract":"Sentiment analysis (SA) is a subfield of natural language processing and data mining which concerns the problem of extracting useful information from users' comments on the Web. Although researchers have been studying different problems in SA for more than one decade, most studies concentrate on English and languages like Persian have not received the attention they deserved. Resource scarcity for assessing sentiment analysis studies is the main limiting factor in Persian. This paper addresses the problem of resource scarcity by introducing two new resources; a sentence-level dataset for sentiment analysis in Persian, SPerSent and a new Persian lexicon, CNRC. SPerSent contains 150000 sentences, each associated with two labels; a binary label indicating the polarity of the sentence, and a five-star rating. These labels are obtained automatically using a lexicon-based method. Specifically, three lexicons are used independently to label each sentence. Then, the majority voting and average methods are used to aggregate the results for polarity and five-star labels, respectively. Finally, a well-known machine learning method, Naïve Bayes, is used to evaluate the SPerSent.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897796","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: 35

Lip-reading via a DNN-HMM hybrid system using combination of the image-based and model-based features 通过结合基于图像和基于模型的特征的DNN-HMM混合系统进行唇读

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983045

Mohammad Hasan Rahmani, F. Almasganj

{"title":"Lip-reading via a DNN-HMM hybrid system using combination of the image-based and model-based features","authors":"Mohammad Hasan Rahmani, F. Almasganj","doi":"10.1109/PRIA.2017.7983045","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983045","url":null,"abstract":"Introducing features that better represent the visual information of speakers during the speech production is still an open issue that highly affects the quality of the lip-reading and Audio Visual Speech Recognition (AVSR) tasks. In this paper, three different types of visual features from both the image-based and model-based ones are investigated inside a professional lip reading task. The simple raw gray level information of the lips Region of Interest (ROI), the geometric representation of lips shape and the Deep Bottle-neck Features (DBNFs) extracted from a 6-layer Deep Auto-encoder Neural Network (DANN) are three valuable feature sets compared while employed for the lip reading purpose. Two different recognition systems, including the conventional GMM-HMM and the state-of-the-art DNN-HMM hybrid, are utilized to perform an isolated and connected digit recognition task. The results indicate that the high level information extracted from deep layers of the lips ROI can represent the visual modality with advantage of “high amount of information in a low dimension feature vector”. Moreover, the DBNFs showed a relative improvement with an average of 15.4% in comparison to the shape features and the shape features showed a relative improvement with an average of 20.4% in comparison to the ROI features over the test data.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163321","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

Wavelet-based variational Bayesian ECG denoising 基于小波的变分贝叶斯心电去噪

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983028

H. Amindavar, F. Naraghi

引用次数: 1

Classification of fNIRS based brain hemodynamic response to mental arithmetic tasks 基于fNIRS的心算任务脑血流动力学反应分类

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983029

A. Rahimpour, A. Dadashi, H. Soltanian-Zadeh, S. Setarehdan

引用次数: 12

Anomaly detection in video using two-part sparse dictionary in 170 FPS 基于两部分稀疏字典的170帧视频异常检测

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983033

S. M. Masoudirad, Jawad Hadadnia

{"title":"Anomaly detection in video using two-part sparse dictionary in 170 FPS","authors":"S. M. Masoudirad, Jawad Hadadnia","doi":"10.1109/PRIA.2017.7983033","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983033","url":null,"abstract":"Automatic supervision of crowd behavior with the aim of detecting abnormal movements has become important in the field of public places' security and protection. Crowd congestion is not fixed in public places, thus we need an algorithm that can perform powerfully in high and low crowd congestions. Generally, there are two different methods for analyzing crowd behavior: the method which is based on tracking moving objects in which every person or moving object is traced separately in the scene, and holistic method which investigates the crowd as a whole. In this paper, our goal is to identify the abnormal behaviors in public places through applying holistic methods (only pedestrians are presents in these places). Crowd behavior is modeled as a collection of basis; identifying and locating the abnormal behaviors are done by Sparse Coding. In real videos, the frames are perspective, and in this research we propose a solution to this problem based on the dataset we use; naming two-part sparse dictionary. General training features are saved in a two-part dictionary, and test movements are analyzed through rebuilding the extracted features from the test video based on the available dictionary which is formed in an unsupervised way using sparse combinations. High errors on this stage show the lack of suitable rebuilding of the test video based on available behaviors in the dictionary, so the algorithm detects and locates abnormal behaviors. Proposed algorithm is performed on UCSD datasets, ROC curve is calculated and EER values are 0.29 and 0.35 respectively. The results show the ability of the proposed algorithm for real time detection of abnormal behaviors.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192680","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

Introducing a new method of image reconstruction against crop attack using sudoku watermarking algorithm 介绍了一种基于数独水印算法的抗裁剪攻击图像重建方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983042

Bahareh Behravan, Alireza Naghsh

引用次数: 2

Classification of human sperm heads using elliptic features and LDA 利用椭圆特征和LDA对人类精子头进行分类

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983036

F. Shaker, S. A. Monadjemi, J. Alirezaie

{"title":"Classification of human sperm heads using elliptic features and LDA","authors":"F. Shaker, S. A. Monadjemi, J. Alirezaie","doi":"10.1109/PRIA.2017.7983036","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983036","url":null,"abstract":"For diagnosis of infertility in men semen analysis is conducted in which sperm morphology i.e. the size and shape of the sperm, is one of the factors that are evaluated. Since manual assessment of sperm morphology is time consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the “within class” differences and “between class” similarities. To automatically classify the sperms, appropriate features should be extracted from their microscopic images. In this research, a set of previously proposed features is extracted and examined in an automatic framework in order to evaluate their discriminating capacity in classifying sperms into four classes of shapes (Normal, Tapered, Pyriform and Amorphous). Also, a new set of features called elliptic features is proposed and added to the original features to improve the classification results. Both sets of features are used with Linear Discriminant Analysis (LDA) classifier. It is shown that adding these new features, significantly improves the discrimination between those classes of sperm shapes.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114351689","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: 8

A multiresolution approach to trajectory description 一种多分辨率的轨迹描述方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983019

Amir Salarpour, Hassan Khotanlou, Mohammad Amin. Mahboubi, S. Daghigh

引用次数: 1