2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)最新文献

Analysis of Optimizers to Regulate Occupant's Actions for Building Energy Management 建筑能源管理中调节居住者行为的优化分析

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-27 DOI: 10.1109/ICAPR.2017.8593024

Monalisa Pal, Raunak Sengupta, S. Bandyopadhyay, A. Alyafi, S. Ploix, P. Reignier, S. Saha

{"title":"Analysis of Optimizers to Regulate Occupant's Actions for Building Energy Management","authors":"Monalisa Pal, Raunak Sengupta, S. Bandyopadhyay, A. Alyafi, S. Ploix, P. Reignier, S. Saha","doi":"10.1109/ICAPR.2017.8593024","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593024","url":null,"abstract":"Occupants and their actions play major roles in building energy management as reported by previous studies, which involves finding the optimal schedule of user actions, under a given physical context, in order to minimize their dissatisfaction. However, comparison and performance analysis of various optimizers, for the concerned problem, have not been studied previously, which is essential to gain insight into the underlying characteristics of the problem. In this work, the performance of four popular and contemporary multi-objective optimization algorithms viz. DEMO, NSGA-II, NSGA-III, and Θ-DEA, for estimating the optimal schedule has been analyzed in terms of their abilities to find minimal average indoor conditions' to discover more number of alternative trade-off solutions (flexibility) and to promptly converge to a smaller minimal net dissatisfaction value (speed of convergence). Results show that NSGA-II has slightly better capabilities than NSGA-III and Θ-DEA, but it clearly outperforms DEMO. The recently developed population dynamics indicators are also applied to support the observed features of the optimizers. The proposed analyzing paradigm can also be used when the optimization problem is extended to include several other objectives.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129802720","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

Binary Classifier Evaluation Without Ground Truth 无基础真值的二分类器评价

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-27 DOI: 10.1109/ICAPR.2017.8593175

M. Fedorchuk, B. Lamiroy

引用次数: 5

3-D Digital flows in cerebrovascular phantoms 脑血管幻象中的三维数字流

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592984

Nirmal Das, Pranati Rakshit, M. Nasipuri, Subhadip Basu

引用次数: 2

Sentimentalizer: Docker container utility over Cloud Sentimentalizer:基于云的Docker容器实用程序

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593104

Krishan Kumar, M. Kurhekar

{"title":"Sentimentalizer: Docker container utility over Cloud","authors":"Krishan Kumar, M. Kurhekar","doi":"10.1109/ICAPR.2017.8593104","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593104","url":null,"abstract":"In this computer era, the most interesting thing is to determine the human opinion using machines. Humans use opinions for conveying their response on a host of things to others. With the increasing popularity and availability of enriching opinion mediums such as personal blogs, forum discussions, online review sites, and micro blogging sites like Twitter, there are new challenges and opportunities for using this information to understand and analyze the sentiments of others. However, web texts usually seem noisy and represent significant issues at the lexical as well as the syntactic level. In this paper, lightweight Docker container is employed over cloud as a utility for sentiment analysis using the four popular classification approaches. It analyzes the reviewer's comment on a product across multiple websites. The analyzed information can be used as a recommendation for the product to a customer. The evaluation process on NLTK benchmark movie review dataset is performed with accuracy, computational cost and resources utilization. The computational analysis shows that our proposed approach meets the requirements of the real time applications over Cloud.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"1 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":"121900480","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: 26

Video Anomaly Detection and Localization using 3D SL-HOF Descriptor 基于3D SL-HOF描述符的视频异常检测与定位

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593005

N. Patil, P. Biswas

{"title":"Video Anomaly Detection and Localization using 3D SL-HOF Descriptor","authors":"N. Patil, P. Biswas","doi":"10.1109/ICAPR.2017.8593005","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593005","url":null,"abstract":"Video anomaly detection plays a prominent and challenging role for automated video surveillance. To aim this, we propose a novel framework for local anomaly detection in videos based on 3D Spatially Localized Histogram of Optical Flow (3D SL-HOF) descriptor. The new 3D SL-HOF motion descriptor is capable of capturing global and local motion variations from spatially distributed optical flow map combined with 3D HOF descriptor which efficiently extracts motion velocity and orientation. Each video is described as a set of nonoverlapping spatio-temporal volumes (STVs) and are further partitioned spatially to form 3D local regions. The histogram of optical flow orientation and motion magnitude extracted from motion-rich STVs used as feature descriptor. To reduce computational burden, we compute features for foreground objects. Simple and cost-effective OCSVM classifier is employed to learn normal behaviour during training and detect anomaly from test data. We define Context location to detect abnormal behaviour in an unexpected region. We demonstrate the performance of the proposed method on the benchmarking UCSD Ped1 and Ped2 local anomaly datasets and UMN crowd activity global anomaly dataset. We achieve promising results and compare the performance with state-of-the-art methods.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"27 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":"132026310","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

Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells 基于浅网络嵌入的白血病细胞分类改进迁移学习

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593186

Kaushik S. Kalmady, Adithya S. Kamath, G. Gopakumar, G. R. S. Subrahmanyam, G. S. Siva

{"title":"Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells","authors":"Kaushik S. Kalmady, Adithya S. Kamath, G. Gopakumar, G. R. S. Subrahmanyam, G. S. Siva","doi":"10.1109/ICAPR.2017.8593186","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593186","url":null,"abstract":"One of the most crucial parts in the diagnosis of a wide variety of ailments is cytopathological testing. This process is often laborious, time consuming and requires skill. These constraints have led to interests in automating the process. Several deep learning based methods have been proposed in this domain to enable machines to gain human expertise. In this paper, we investigate the effectiveness of transfer learning using fine-tuned features from modified deep neural architectures and certain ensemble learning methods for classifying the leukemia cell lines HL60, MOLT, and K562. Microfluidics-based imaging flow cytometry (mIFC) is used for obtaining the images instead of image cytometry. This is because mIFC guarantees significantly higher throughput and is easy to set up with minimal expenses. We find that the use of fine-tuned features from a modified deep neural network for transfer learning provides a substantial improvement in performance compared to earlier works. We also identify that without any fine tuning, feature selection using ensemble methods on the deep features also provide comparable performance on the considered Leukemia cell classification problem. These results show that automated methods can in fact be a valuable guide in cytopathological testing especially in resource limited settings.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"18 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":"130268208","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

A two phase trained Convolutional Neural Network for Handwritten Bangla Compound Character Recognition 一种用于手写体孟加拉复合字识别的两阶段训练卷积神经网络

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592983

Prateek Keserwani, T. Ali, P. Roy

引用次数: 4

Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination 基于透射率图和环境光照联合估计的图像去雾

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593161

Sanchayan Santra, Ranjan Mondal, Pranoy Panda, N. Mohanty, Shubham Bhuyan

{"title":"Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination","authors":"Sanchayan Santra, Ranjan Mondal, Pranoy Panda, N. Mohanty, Shubham Bhuyan","doi":"10.1109/ICAPR.2017.8593161","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593161","url":null,"abstract":"Haze limits the visibility of outdoor images, due to the existence of fog, smoke and dust in the atmosphere. Image dehazing methods try to recover haze-free image by removing the effect of haze from a given input image. In this paper, we present an end to end system, which takes a hazy image as its input and returns a dehazed image. The proposed method learns the mapping between a hazy image and its corresponding transmittance map and the environmental illumination, by using a multi-scale Convolutional Neural Network. Although most of the time haze appears grayish in color, its color may vary depending on the color of the environmental illumination. Very few of the existing image dehazing methods have laid stress on its accurate estimation. But the color of the dehazed image and the estimated transmittance depends on the environmental illumination. Our proposed method exploits the relationship between the transmittance values and the environmental illumination as per the haze imaging model and estimates both of them. Qualitative and quantitative evaluations show, the estimates are accurate enough.","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":"115736239","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

Uncertain Information Classification: A Four-Way Decision Making Approach 不确定信息分类:一种四向决策方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593087

Pritpal Singh, Kinjal Rabadiya

引用次数: 2

FACE RECOGNITION from NON-FRONTAL IMAGES Using DEEP NEURAL NETWORK 基于深度神经网络的非正面图像人脸识别

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593160

S. Chowdhury, J. Sil

{"title":"FACE RECOGNITION from NON-FRONTAL IMAGES Using DEEP NEURAL NETWORK","authors":"S. Chowdhury, J. Sil","doi":"10.1109/ICAPR.2017.8593160","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593160","url":null,"abstract":"Person recognition from pose-variant face images is a well addressed, yet challenging problem, especially for surveillance in a crowded place where the pose variation is large in the test set compare to the training set. Conventional feature extraction based face recognition techniques are not efficient enough to solve the problem. In this paper, a noble mechanism has been proposed to learn the training set consisting of few pose variant images and many frontal images of different persons using deep learning algorithms. At first, autoencoders are trained to build the templates for representing the pose variant training images. The left (45°) and right (+45°) templates cover all pose variations of test images from 90° to +90°. In the next step the convolution neural network (CNN) architectures are used in supervised mode for transforming the templates into person specific frontal images present in the training set. Left and right cluster of trained CNNs are obtained with respect to left and right templates. In the testing phase, the head-pose of the test image is estimated using collaborative representation based classifier (CRC) in order to select the appropriate cluster of CNN architectures for generation of the frontal image. The CNN architecture which provides the best match frontal image with the training set is recognized as the specific person. The matching score is measured using correlation coefficient and Frobenius norm. For a frontal test image if the matching score is below than the predefined threshold then the proposed method does not recognize the image. However, the training set has been updated by the unrecognized frontal test images for future recognition. The accuracy of the proposed method is around 99% when tested on CMU PIE database which is much higher in comparison to the existing face-recognition methods.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"514 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":"124475389","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