This paper covers an experiment that investigates the relation between the quality of the dataset and the performance of the classifier. It demonstrates that dataset with less noisy labels, i.e., higher agreement level between labelers can achieve better classification accuracy results. In order to set the experiment, we divided human annotated Arabic Twitter dataset under two levels of majority voting ratio: low and high. Then, we reported the credibility prediction accuracy results under these two levels. It was found that by using labeled dataset with low level of agreement between labelers means low ratio of majority voting class, the accuracy was in the range (32% - 50.5%) whereas with labeled dataset with high percentage of majority voting class, it was between (62.8% - 66.7%). This finding clarifies that improving the quality of labeling by reducing the effect of noisy labels would yield better classification results.
{"title":"Labeling Agreement Level and Classification Accuracy","authors":"Amal Abdullah Al Mansour","doi":"10.1109/SITIS.2016.51","DOIUrl":"https://doi.org/10.1109/SITIS.2016.51","url":null,"abstract":"This paper covers an experiment that investigates the relation between the quality of the dataset and the performance of the classifier. It demonstrates that dataset with less noisy labels, i.e., higher agreement level between labelers can achieve better classification accuracy results. In order to set the experiment, we divided human annotated Arabic Twitter dataset under two levels of majority voting ratio: low and high. Then, we reported the credibility prediction accuracy results under these two levels. It was found that by using labeled dataset with low level of agreement between labelers means low ratio of majority voting class, the accuracy was in the range (32% - 50.5%) whereas with labeled dataset with high percentage of majority voting class, it was between (62.8% - 66.7%). This finding clarifies that improving the quality of labeling by reducing the effect of noisy labels would yield better classification results.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026611","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}
Cloud computing is the fruit of recent developments in information technology, it provides access to many online services as well as remote computing resources as needed. To be more specific, cloud computing stands today as a satisfactory answer to the problem of storage and computing of data encountered by companies. It provides treatment and accommodation of their digital information via a fully outsourced infrastructure. The latter enables users to benefit from many online services without worrying about the technical aspects of their use. In the meanwhile, it limits costs generated by the management of these data. However, this advanced technology has immediately highlighted many serious security troubles. The major issue that prevents many companies to migrate to the cloud is the security of sensitive data hosted in the provider. Actually, the security problem related to this technology has slowed their expansion and restricted in a severe way their scope. The work in this paper deals to present a literature review of data security approaches for cloud computing, and evaluates them in terms of how well they support critical security services and what level of adaptation they achieve.
{"title":"Security Issues in Cloud Computing and Associated Alleviation Approaches","authors":"Hamza Hammami, H. Brahmi, Imen Brahmi, S. Yahia","doi":"10.1109/SITIS.2016.125","DOIUrl":"https://doi.org/10.1109/SITIS.2016.125","url":null,"abstract":"Cloud computing is the fruit of recent developments in information technology, it provides access to many online services as well as remote computing resources as needed. To be more specific, cloud computing stands today as a satisfactory answer to the problem of storage and computing of data encountered by companies. It provides treatment and accommodation of their digital information via a fully outsourced infrastructure. The latter enables users to benefit from many online services without worrying about the technical aspects of their use. In the meanwhile, it limits costs generated by the management of these data. However, this advanced technology has immediately highlighted many serious security troubles. The major issue that prevents many companies to migrate to the cloud is the security of sensitive data hosted in the provider. Actually, the security problem related to this technology has slowed their expansion and restricted in a severe way their scope. The work in this paper deals to present a literature review of data security approaches for cloud computing, and evaluates them in terms of how well they support critical security services and what level of adaptation they achieve.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114753545","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}
Alaettin Uçan, Behzad Naderalvojoud, E. Sezer, H. Sever
This paper proposes an automatic translation approach to create a sentiment lexicon for a new language from available English resources. In this approach, an automatic mapping is generated from a sense-level resource to a wordlevel by applying a triple unification process. This process produces a single polarity score for each term by incorporating all sense polarities. The major idea is to deal with the sense ambiguity during the lexicon transfer and provide a general sentiment lexicon for languages like Turkish which do not have a freely available machine-readable dictionary. On the other hand, the translation quality is critical in the lexicon transfer due to the ambiguity problem. Thus, this paper also proposes a multiple bilingual translation approach to find the most appropriate equivalents for the source language terms. In this approach, three parallel, series and hybrid algorithms are used to integrate the translation results. Finally, three lexicons are achieved for the target language with different sizes. The performance of three lexicons is evaluated in the lexicon-based sentiment classification task and compared with the results achieved by the supervised approach. According to experimental results, the proposed approach can produce reliable sentiment lexicons for the target language.
{"title":"SentiWordNet for New Language: Automatic Translation Approach","authors":"Alaettin Uçan, Behzad Naderalvojoud, E. Sezer, H. Sever","doi":"10.1109/SITIS.2016.57","DOIUrl":"https://doi.org/10.1109/SITIS.2016.57","url":null,"abstract":"This paper proposes an automatic translation approach to create a sentiment lexicon for a new language from available English resources. In this approach, an automatic mapping is generated from a sense-level resource to a wordlevel by applying a triple unification process. This process produces a single polarity score for each term by incorporating all sense polarities. The major idea is to deal with the sense ambiguity during the lexicon transfer and provide a general sentiment lexicon for languages like Turkish which do not have a freely available machine-readable dictionary. On the other hand, the translation quality is critical in the lexicon transfer due to the ambiguity problem. Thus, this paper also proposes a multiple bilingual translation approach to find the most appropriate equivalents for the source language terms. In this approach, three parallel, series and hybrid algorithms are used to integrate the translation results. Finally, three lexicons are achieved for the target language with different sizes. The performance of three lexicons is evaluated in the lexicon-based sentiment classification task and compared with the results achieved by the supervised approach. According to experimental results, the proposed approach can produce reliable sentiment lexicons for the target language.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116997361","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}
Recently, dense trajectories were shown to be an efficient video motion representation for action recognition and achieved state-of-the-art results on a variety of video datasets. This paper improves their performance by taking into account camera motion. To estimate camera motion, the authors use long-term point trajectory analysis to cluster image points and propose an algorithm to find possible background cluster from these clusters according to background nature in a video. Considering the original clusters could not segment the foreground and background very well. The authors optimize the background cluster, and use the cluster to rectify the trajectory. Experimental results on three challenging action datasets (i.e., Hollywood2, Olympic Sports and UCF50) show that the rectified trajectories significantly outperform original dense trajectories.
{"title":"Action Recognition for Videos by Long-Term Point Trajectory Analysis with Background Removal","authors":"Yuze Xiang, Y. Okada, Kosuke Kaneko","doi":"10.1109/SITIS.2016.13","DOIUrl":"https://doi.org/10.1109/SITIS.2016.13","url":null,"abstract":"Recently, dense trajectories were shown to be an efficient video motion representation for action recognition and achieved state-of-the-art results on a variety of video datasets. This paper improves their performance by taking into account camera motion. To estimate camera motion, the authors use long-term point trajectory analysis to cluster image points and propose an algorithm to find possible background cluster from these clusters according to background nature in a video. Considering the original clusters could not segment the foreground and background very well. The authors optimize the background cluster, and use the cluster to rectify the trajectory. Experimental results on three challenging action datasets (i.e., Hollywood2, Olympic Sports and UCF50) show that the rectified trajectories significantly outperform original dense trajectories.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097066","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}
This research aimed to the study analysis knee angle of color set detection using image processing technique. It had been developed to assist the patient after knee joint surgery or those who had knee joint problem by detecting value from color set to record result for treatment planning and shortening time to diagnose or set knee joint treatment planning for physician. This research utilized the video camera to record the knee joint therapy and displayed the color detection real-time result on computer. Connected-component labeling and bounding box techniques using Open CV program to design color detector from color set software which displayed the current angle and recorded the patient data as the new patient and existing patient. Results from three patients showed that the analysis system of knee joint angle was useable. The accuracy of program was 96% and the system could record the patient data as new and existing patient. Moreover, the system could be applied to analyze the condition or treatment planning for the physician.
{"title":"The Study Analysis Knee Angle of Color Set Detection Using Image Processing Technique","authors":"Patiyuth Pramkeaw","doi":"10.1109/SITIS.2016.109","DOIUrl":"https://doi.org/10.1109/SITIS.2016.109","url":null,"abstract":"This research aimed to the study analysis knee angle of color set detection using image processing technique. It had been developed to assist the patient after knee joint surgery or those who had knee joint problem by detecting value from color set to record result for treatment planning and shortening time to diagnose or set knee joint treatment planning for physician. This research utilized the video camera to record the knee joint therapy and displayed the color detection real-time result on computer. Connected-component labeling and bounding box techniques using Open CV program to design color detector from color set software which displayed the current angle and recorded the patient data as the new patient and existing patient. Results from three patients showed that the analysis system of knee joint angle was useable. The accuracy of program was 96% and the system could record the patient data as new and existing patient. Moreover, the system could be applied to analyze the condition or treatment planning for the physician.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123706553","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}
Scaling up video resolution has conventionally been achieved via linear interpolation, however this method occasionally introduces blurring to the output. Super-resolution (SR), an approach to preserve image quality in enlarged still images, has been exploited as a substitute for linear interpolation, however, the output at times exhibits image qualities worse than what linear interpolation produces primarily because the initial goal of SR is preservation of image quality when a still image is enlarged. In this context, this paper proposes a fast-performance adaptive system for scaling-up other resolutions like X2 using X3 model or X3 using X2 model by (1) first grouping frames that would use similar filter sets (2) then conducting fine-tuning of shallow CNN for SR on each frame group. Filter sets fine-tuned for each group resulted in significantly improved PSNR over either linear interpolation or conventional SR in our experiment. In the fine-tuning stage for each group, 0.5K to 2.5K iterations were sufficient to improve PSNR by 10%. By fine-tuning instead of performing full training, the number of sufficient iterations was reduced from 3,000K to mere 0.5K to 2.5K.
{"title":"An Enhanced Video Super Resolution System Using Group-Based Optimized Filter-Set with Shallow Convolutional Neural Network","authors":"Sangchul Kim, J. Nang","doi":"10.1109/SITIS.2016.59","DOIUrl":"https://doi.org/10.1109/SITIS.2016.59","url":null,"abstract":"Scaling up video resolution has conventionally been achieved via linear interpolation, however this method occasionally introduces blurring to the output. Super-resolution (SR), an approach to preserve image quality in enlarged still images, has been exploited as a substitute for linear interpolation, however, the output at times exhibits image qualities worse than what linear interpolation produces primarily because the initial goal of SR is preservation of image quality when a still image is enlarged. In this context, this paper proposes a fast-performance adaptive system for scaling-up other resolutions like X2 using X3 model or X3 using X2 model by (1) first grouping frames that would use similar filter sets (2) then conducting fine-tuning of shallow CNN for SR on each frame group. Filter sets fine-tuned for each group resulted in significantly improved PSNR over either linear interpolation or conventional SR in our experiment. In the fine-tuning stage for each group, 0.5K to 2.5K iterations were sufficient to improve PSNR by 10%. By fine-tuning instead of performing full training, the number of sufficient iterations was reduced from 3,000K to mere 0.5K to 2.5K.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124234533","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}
Standard principal component analysis (PCA) is frequently applied to a set of 1D vectors. For a set of 2D objects such as images, a 2DPCA approach that computes principal components of row-row and column-column covariance matrices would be more appropriate. A new 2DPCA method for low numerical rank matrices and based on orthogonal triangular (QR) factorization is proposed in this paper. The QR-based 2DPCA displays more efficiency in terms of computational complexity. We also propose and discuss a new updating schema for 2DPCA called 2DIPCA showcasing its numerical stability and speed. The proposed methods are applied to image compression and recognition and show their outperformances over a bunch of 1D and 2D PCA methods in both the batch and incremental modes. Experiments are performed on three benchmark face databases. Results reveal that the proposed methods achieve relatively substantial results in terms of recognition accuracy, compression rate and speed.
{"title":"An Incremental Two-Dimensional Principal Component Analysis for Image Compression and Recognition","authors":"H. Nakouri, M. Limam","doi":"10.1109/SITIS.2016.121","DOIUrl":"https://doi.org/10.1109/SITIS.2016.121","url":null,"abstract":"Standard principal component analysis (PCA) is frequently applied to a set of 1D vectors. For a set of 2D objects such as images, a 2DPCA approach that computes principal components of row-row and column-column covariance matrices would be more appropriate. A new 2DPCA method for low numerical rank matrices and based on orthogonal triangular (QR) factorization is proposed in this paper. The QR-based 2DPCA displays more efficiency in terms of computational complexity. We also propose and discuss a new updating schema for 2DPCA called 2DIPCA showcasing its numerical stability and speed. The proposed methods are applied to image compression and recognition and show their outperformances over a bunch of 1D and 2D PCA methods in both the batch and incremental modes. Experiments are performed on three benchmark face databases. Results reveal that the proposed methods achieve relatively substantial results in terms of recognition accuracy, compression rate and speed.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126185214","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}
Tourism is one of the most benefitted areas of internet and its related progressive technologies. Smart tourism requires bringing together the various stakeholders in the tourism industry through a common platform of technology. This paper aims to provide an insight into this concept, we firstly introduce the technological foundation of smart tourism system, and then we propose an electronic ticketing system that can be is proposed to integrate the information of events that could interest tourist. The benefits and challenges which may occur or have occurred in the implementation of smart tourism are finally discussed.
{"title":"Design of Electronic Ticket System for Smart Tourism","authors":"Anouar Dalli, S. Bri","doi":"10.1109/SITIS.2016.82","DOIUrl":"https://doi.org/10.1109/SITIS.2016.82","url":null,"abstract":"Tourism is one of the most benefitted areas of internet and its related progressive technologies. Smart tourism requires bringing together the various stakeholders in the tourism industry through a common platform of technology. This paper aims to provide an insight into this concept, we firstly introduce the technological foundation of smart tourism system, and then we propose an electronic ticketing system that can be is proposed to integrate the information of events that could interest tourist. The benefits and challenges which may occur or have occurred in the implementation of smart tourism are finally discussed.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128236917","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}
Recent literatures have illustrated approaches that can automatically extract informative content from noisy mobile app reviews, however the key information such as feature requests, bug reports etc., retrieved by these methods are still mixed and what users really care about the app remains unknown to developers. In this paper we propose a novel model SAR: Stratify App Reviews, providing developers information about users' real reaction toward apps. SAR stratifies informative reviews into different layers, grouping the reviews based on what users concern, and we also develop a method to compute the user general sentiment on each entity. The model performs user-oriented analytics from raw reviews by (i) first extracting entities from each review, identifying hot entities of the app that users mostly care about, (ii) then stratifying all the reviews into different layers according to hot entities with a four-layer Bayes probability method, (iii) and finally computing user sentiments on hot entities. We conduct experiments on three genres of apps i.e. Games, Social, and Media, the result shows that SAR could identify different hot entities with respect to the specific categories of apps, and accordingly, it can stratify relevant reviews into different layers, the sentiment value of each entity can also represent users' satisfaction well, we also compared the result with human analysis, with the similar accuracy, the SAR can speed up the overall analysis automatically. Our model can help developers quickly understand what entities of the app users mostly care about, and how do they react to these entities.
{"title":"Stratify Mobile App Reviews: E-LDA Model Based on Hot \"Entity\" Discovery","authors":"Y. Liu, Yanwei Li, Yanhui Guo, Miao Zhang","doi":"10.1109/SITIS.2016.97","DOIUrl":"https://doi.org/10.1109/SITIS.2016.97","url":null,"abstract":"Recent literatures have illustrated approaches that can automatically extract informative content from noisy mobile app reviews, however the key information such as feature requests, bug reports etc., retrieved by these methods are still mixed and what users really care about the app remains unknown to developers. In this paper we propose a novel model SAR: Stratify App Reviews, providing developers information about users' real reaction toward apps. SAR stratifies informative reviews into different layers, grouping the reviews based on what users concern, and we also develop a method to compute the user general sentiment on each entity. The model performs user-oriented analytics from raw reviews by (i) first extracting entities from each review, identifying hot entities of the app that users mostly care about, (ii) then stratifying all the reviews into different layers according to hot entities with a four-layer Bayes probability method, (iii) and finally computing user sentiments on hot entities. We conduct experiments on three genres of apps i.e. Games, Social, and Media, the result shows that SAR could identify different hot entities with respect to the specific categories of apps, and accordingly, it can stratify relevant reviews into different layers, the sentiment value of each entity can also represent users' satisfaction well, we also compared the result with human analysis, with the similar accuracy, the SAR can speed up the overall analysis automatically. Our model can help developers quickly understand what entities of the app users mostly care about, and how do they react to these entities.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130895271","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}
Recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. The problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary Pattern (LBP) approach, which is frequently used in identifying textures in images, in one dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. The experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.
{"title":"Texture of Activities: Exploiting Local Binary Patterns for Accelerometer Data Analysis","authors":"Tunç Aşuroğlu, K. Açıcı, Ç. Erdaş, H. Oğul","doi":"10.1109/SITIS.2016.29","DOIUrl":"https://doi.org/10.1109/SITIS.2016.29","url":null,"abstract":"Recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. The problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary Pattern (LBP) approach, which is frequently used in identifying textures in images, in one dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. The experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127083266","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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