2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)最新文献_第2页

A Style Preserving Shape Representation Scheme for Handwritten Gesture Recognition 一种手写手势识别的保持风格的形状表示方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593187

P. Viswanath, V. Sekhar

{"title":"A Style Preserving Shape Representation Scheme for Handwritten Gesture Recognition","authors":"P. Viswanath, V. Sekhar","doi":"10.1109/ICAPR.2017.8593187","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593187","url":null,"abstract":"Handwritten pen or finger based inputs are very common, especially with hand-held devices like mobile phones or PDAs. Apart from the content, there is the writing style information embedded in to the handwritten input. They are useful as personalized command gestures, passwords or as authentication supplements. Since the input is assumed to be written by a finger on the touch screen of the hand-held device, the input considered is a trajectory over the (x,y, t) space where x and y are the spacial and t is the time co-ordinate. The hand held device can be held in a variety of ways while giving the input, and size of the input may also vary. Normalizing with respect to rotation is an error prone process. Also, the other important limitation is that the number of training examples could be very few. The paper presents a novel style preserving TRS (translation, rotation and scale) invariant representation scheme for the 3D trajectory data that use 2D Zernike moments only and is called the time varying Zernike moments (TVZMs). For this, we present a 2D representation of the 3D function and show that it is lossless. The proposed representation scheme is sensitive to both content and to the writing style. Experimentally it is shown that 2D TVZMs are superior than 3D Zernike moments which possess same invariance properties. Also a comparison is drawn with a recent online signature representation scheme. Hence, it may be a suitable one for personalized command gesture recognition.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125769886","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: 0

An Ellipse Fitted Training-Less Model for Pedestrian Detection 一种椭圆拟合无训练行人检测模型

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592967

Arindam Sikdar, A. Chowdhury

引用次数: 4

Study on Applicability of Curriculum Framework to Feature Selection Algorithms 课程框架对特征选择算法的适用性研究

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593126

Deepthi Kalavala, C. Bhagvati

{"title":"Study on Applicability of Curriculum Framework to Feature Selection Algorithms","authors":"Deepthi Kalavala, C. Bhagvati","doi":"10.1109/ICAPR.2017.8593126","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593126","url":null,"abstract":"The present work demonstrates the applicability of consistency based and entropy based feature selection metrics to curriculum framework. Feature selection aims at reducing the dimensionality of datasets and is a preprocessing step for classification and clustering. In general, feature selection algorithms make use of the entire training set for identifying important features. Curriculum based feature selection uses instances in the order of their complexity in an incremental paradigm. Curriculum framework deals with easy and then difficult instances in a guided environment. Curriculum methods are advantageous over no-curriculum methods in two ways. Firstly, the time consumed by curriculum methods is very less; secondly, curriculum methods allow the feature selection methods to be applied in incremental paradigm. Incremental nature of curriculum methods allows feature selection to work upon newly added samples in less time. In this paper, curriculum methods are compared with no-curriculum methods using performance indices - classification accuracy, time and selection ratio. Experimental results on various datasets show (varying degrees of) superiority of using curriculum framework in feature selection.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448982","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: 0

Improving Isolated Bangla Compound Character Recognition Through Feature-map Alignment 通过特征图对齐改进孤立孟加拉语复合字识别

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593008

Pinaki Ranjan Sarkar, Deepak Mishra, Gorthi R. K. S. S. Manyam

{"title":"Improving Isolated Bangla Compound Character Recognition Through Feature-map Alignment","authors":"Pinaki Ranjan Sarkar, Deepak Mishra, Gorthi R. K. S. S. Manyam","doi":"10.1109/ICAPR.2017.8593008","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593008","url":null,"abstract":"Due to high variability in writing style of different individuals, non-centered and non-uniformly scaled optical characters are very difficult to recognize. Several techniques are proposed in-order to solve the recognition problem. In this work, we highlight that the performance of optical character classifiers which are based on the deep learning framework can be improved through feature-map alignment. Here, we have used spatial transformer network to align the feature maps of a convolutional neural network model which is proposed for the classification problem. We demonstrate that with the proposed framework not only the slight transformed versions which are usually considered in the conventional datasets can be classified with high accuracy, but also highly non-uniform in scale characters can also be fairly recognized with quite higher accuracy. We evaluate our proposed model on CMATERdb 3.1.3 database which consists of isolated Bangla handwritten compound characters and our model obtained 97.86 % recognition accuracy in the original database and 96.34 % on various rotated data in training and testing.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121315306","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

Spatio-temporal Prediction under Scarcity of Influencing Variables: A Hybrid Probabilistic Graph-based Approach 影响变量稀缺条件下的时空预测:一种基于混合概率图的方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593054

Monidipa Das, S. Ghosh

{"title":"Spatio-temporal Prediction under Scarcity of Influencing Variables: A Hybrid Probabilistic Graph-based Approach","authors":"Monidipa Das, S. Ghosh","doi":"10.1109/ICAPR.2017.8593054","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593054","url":null,"abstract":"Spatio-temporal (ST) prediction is one of the major families of spatio-temporal data mining, which aims at learning a model that can predict the target variable by using a given set of explanatory variables in a spatio-temporal framework. It has enormous application in various domains, including environmental management, transportation, epidemiology, climatology and so on. However, a major issue in ST prediction of any variable is the unknown factors that can have significant influence on it, or the unavailability of the data on the factors that influence the prediction variable. In such cases, due to lack of appropriate data on influencing factors, the effectiveness of many of the existing space-time prediction models, especially those based on causal graph-based approach, are significantly hindered. In the present paper, an attempt has been made to address this issue by proposing a hybrid probabilistic model based on fuzzy Bayesian network with incorporated residual correction mechanism (FBNRC). The incorporated fuzziness aids in reducing uncertainty during parameter learning process, whereas the added functionality of residual correction helps to compensate for the unknown factors while generating inference on the prediction variable. Experimentation has been carried out on spatio-temporal prediction of meteorological time series in the state of Delhi, India. The results of prediction are found to be encouraging.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"35 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121012465","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

Location Aware Hierarchical Cell-Based Anomaly Detection and Categorization in Crowded Scenes 拥挤场景中基于位置感知分层单元的异常检测与分类

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592959

N. Patil, P. Biswas

{"title":"Location Aware Hierarchical Cell-Based Anomaly Detection and Categorization in Crowded Scenes","authors":"N. Patil, P. Biswas","doi":"10.1109/ICAPR.2017.8592959","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8592959","url":null,"abstract":"In this paper, we propose a novel framework for anomaly detection and categorization in crowded scenes via location aware hierarchical cell-based (LAHCB) approach. Input video frames are split into spatio-temporal volumes (STVs). Low-level histogram of optical flow orientation and motion magnitude features are extracted from selective STVs (SSTVs) at three levels of hierarchy achieving a coarse-to-the-fine localization. We use one-class SVM (OCSVM) classifier to model normal events and detect anomaly. The proposed method consists of two parts: (1) Global Analysis for frame-level detection; (2) Local Analysis for pixel-level localization. For global analysis, we adopt computationally less expensive model that uses only coarser level. Further, we use this information to achieve localization. The computational efficiency lies in faster online testing since storage and time overhead is due to offline feature extraction and classification at different levels in the hierarchical structure during training phase. Unlike existing methods, the proposed approach omits pixel level feature computation and background modeling. The addition of location aware concept detects abnormal behaviour in an unexpected region. We demonstrate the performance of the proposed method on the UCSD and UMN datasets. We achieve AUC of 86.16% and 86.3% on Ped1 and Ped2, comparable with state-of-the-art methods.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123788664","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: 0

A Modular Approach for Facial Expression Recognition using HSOG 基于HSOG的面部表情识别模块化方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592988

Sujata, S. Mitra

{"title":"A Modular Approach for Facial Expression Recognition using HSOG","authors":"Sujata, S. Mitra","doi":"10.1109/ICAPR.2017.8592988","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8592988","url":null,"abstract":"Automatic facial expression recognition is one of the most recently topic in aspect of behaviour analysis and human computer interaction (HCI). Difficulty with facial expression recognition system is to implement generic model. Same facial expression may vary across humans, even this is true for the same person when the expression is displayed in different situations. This paper proposed the local image descriptor that extracts the histogram of second order gradients (HSOG), which capture the local curvatures of differential geometry. The shape index is computed from the curvatures and its different values correspond to different shapes. In case of facial expression recognition using full face images, if any portion of the face image is distorted, it may reflect on the recognition performance. Humans have the capability to recognize faces even by looking at some parts of the face. An attempt has been made to replicate the same on machines by only considering some of the informative regions of the face like eyes, nose, lip and forehead. Facial expression recognition experiments have been performed on some benchmark databases, Better recognition rates were achieved compared to other existing approaches.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134401309","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

Novel Energy Separation Based Frequency Modulation Features for Spoofed Speech Classification 基于能量分离的新型调频特征用于欺骗语音分类

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593041

Madhu R. Kamble, H. Patil

{"title":"Novel Energy Separation Based Frequency Modulation Features for Spoofed Speech Classification","authors":"Madhu R. Kamble, H. Patil","doi":"10.1109/ICAPR.2017.8593041","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593041","url":null,"abstract":"Speech Synthesis (SS) and Voice Conversion (VC) methods provides a great risk for Automatic Speaker Verification (ASV) system. In this paper, we tried to find the difference between natural and spoofed speech signals using Teager Energy Operator-based Energy Separation Algorithm (TEO-ESA). Here, we exploit the contribution of Amplitude Envelope (AE) and Instantaneous Frequency (IF) in each narrowband filtered signals energy via ESA to capture possible changes in a temporal and spectral envelope of the synthetic speech signal generated by the machines as opposed to natural signals. Furthermore, IF was used for classification of natural vs. spoof speech with Gaussian Mixture Model (GMM) as a classifier. These findings may assist to distinguish these two speeches and provide an aid to alleviate possible impostor attacks in voice biometrics. The experiments are done on ASV Spoof 2015 Challenge database. We have compared proposed Energy Separation Algorithm-Instantaneous Frequency Cosine Coefficients (ESA-IFCC) with Mel Frequency Cepstral Coefficients (MFCC) features. On the development set, MFCC alone gave an Equal Error Rate (EER) of (6.98 %) and ESA-IFCC gave (5.43 %) with 13-D static features. With score-level fusion of MFCC and ESA-IFCC EER reduced to 3.45 % on static feature vector. The EER decreases further to 2.01 % and 1.89 % for Δ and ΔΔ features. On evaluation set, the overall average error rate for known and unknown attacks was 6.79 % for ESA-IFCC and was significantly better than the MFCC (9.15 %) and their score-level fused EER (7.16 %).","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524159","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

Two Stage Zero-resource Approaches for QbE-STD QbE-STD的两阶段零资源方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593033

Maulik C. Madhavi, H. Patil

引用次数: 0

Performance Analysis of Image Segmentation for Oral Tissue 口腔组织图像分割性能分析

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593139

Archana A. Nawandhar, Lakshmi Yamujala, Navin Kumar

{"title":"Performance Analysis of Image Segmentation for Oral Tissue","authors":"Archana A. Nawandhar, Lakshmi Yamujala, Navin Kumar","doi":"10.1109/ICAPR.2017.8593139","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593139","url":null,"abstract":"Digital image segmentation is the first step in computer aided diagnostic procedures which are carried out with the help of medical images in the medical field. In this paper, segmentation of Hematoxylin and Eosin (H&E)-stained microscopic image of stratified squamous epithelial layer of oral cavity to separate the squamous cells from the background is performed by different approaches. Due to the complex structure of the squamous epithelial layer, the widely used K-means clustering and thresholding techniques are either not suitable for segmenting such images or unable to furnishthe suitable result. In this work, we are proposing new method for segmentation using Gabor filter. The input image is filtered through a bank of Gabor filters. The number of scales used in constructing the bank of filters is adaptive and automatically computed based on the size of the image. Filtered outputs are taken as 2-dimentional feature vectors. Furthermore, principal component analysis is performed to reduce the dimensionality. In addition, the first principal component is used as the feature image for further processing towards segmentation. This feature image is given as input to both the K-means clustering and thresholding for the final segmentation. The outputs of different approaches are compared. It is found that Gabor filter with thresholding and K-means clustering offers improved result as compared to the conventional ones.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126296196","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