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":"26 1","pages":"0"},"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}
The pulse wave signal contains a variety of noises and has strong nonlinear and non-stationary. According to the previous wavelet transformation method, this paper proposes a PPG signal de-noising algorithm based on dual-tree complex wavelet transform (DTCWT) and morphological filtering. With the advantage of simple construction, clear mathematical implications and low computational complexity, this method overcomes the shift sensitive and the frequency aliasing in the discrete wavelet transform. The simulation results show that this algorithm could remove the power line interference and EMG interference, and the quantitative index of SNR and mean square error is superior to the traditional threshold de-noising algorithm. Therefore, the DTCWT and morphological filtering de-noising algorithm would obtain a clear pulse wave signal.
{"title":"A PPG Signal De-Noising Method Based on the DTCWT and the Morphological Filtering","authors":"Tong Bai, Dan Li, Huiqian Wang, Yu Pang, Guoquan Li, Jinzhao Lin, Q. Zhou, Gwanggil Jeon","doi":"10.1109/SITIS.2016.85","DOIUrl":"https://doi.org/10.1109/SITIS.2016.85","url":null,"abstract":"The pulse wave signal contains a variety of noises and has strong nonlinear and non-stationary. According to the previous wavelet transformation method, this paper proposes a PPG signal de-noising algorithm based on dual-tree complex wavelet transform (DTCWT) and morphological filtering. With the advantage of simple construction, clear mathematical implications and low computational complexity, this method overcomes the shift sensitive and the frequency aliasing in the discrete wavelet transform. The simulation results show that this algorithm could remove the power line interference and EMG interference, and the quantitative index of SNR and mean square error is superior to the traditional threshold de-noising algorithm. Therefore, the DTCWT and morphological filtering de-noising algorithm would obtain a clear pulse wave signal.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130771505","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":"28 1","pages":"0"},"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}
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":"70 1","pages":"0"},"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}
H. Jomaa, Mohamad Kamereddine, A. Nayal, Yara Rizk, M. Awad
Comic books are considered a heritage in many countries. The colorful depiction of annotated events has gained an increasing amount of interest over the past decade as the digitization process took over printed media, in addition to the abundance and variety of available data. Several applications have been devised in the field of computer vision and natural language processing to handle comic book pages. However, in this paper, we focus on the connection between the two, specifically, we compare the emotions that color and text separately imply. The theory of color and its effect on one's emotional state can be dated back to the 1800's. The language used in speech balloons is also written to a way to capture the audience's attention and manipulate their emotions. Throughout this paper, the color theory is applied to analyze the pages' emotional implication, and compared to the output of minSVM, a modified SVM classifier that accommodates imbalanced datasets, and a regular SVM, that are trained and implemented on the extracted text of a homegrown database to identify the emotions they convey. Using minSVM, we obtained a 91.26 % accuracy as opposed to an 89.66 % with SVM.
{"title":"Affective Relationship between Color and Text in Arabic Comic Books","authors":"H. Jomaa, Mohamad Kamereddine, A. Nayal, Yara Rizk, M. Awad","doi":"10.1109/SITIS.2016.42","DOIUrl":"https://doi.org/10.1109/SITIS.2016.42","url":null,"abstract":"Comic books are considered a heritage in many countries. The colorful depiction of annotated events has gained an increasing amount of interest over the past decade as the digitization process took over printed media, in addition to the abundance and variety of available data. Several applications have been devised in the field of computer vision and natural language processing to handle comic book pages. However, in this paper, we focus on the connection between the two, specifically, we compare the emotions that color and text separately imply. The theory of color and its effect on one's emotional state can be dated back to the 1800's. The language used in speech balloons is also written to a way to capture the audience's attention and manipulate their emotions. Throughout this paper, the color theory is applied to analyze the pages' emotional implication, and compared to the output of minSVM, a modified SVM classifier that accommodates imbalanced datasets, and a regular SVM, that are trained and implemented on the extracted text of a homegrown database to identify the emotions they convey. Using minSVM, we obtained a 91.26 % accuracy as opposed to an 89.66 % with SVM.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368044","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}
Traditional cryptosystems are based on the possession of secret keys that can be stolen or shared by non legitimate users. On the other hand, binding the real identity of a system user to what he is instead of something he knows or possesses is the main desirable property of biometric systems. Biometric Crypto-Systems (or BCSs) are designed to bind a cryptographic key with a biometric template to ensure that only a legitimate user can access to encrypted data. In this paper, we propose a new biometric cryptosystem that reformulates the binding process as a minimization problem in such a way that the cryptographic key cannot be derived from the parameters of the objective functions unless the system is provided with a valid biometric template. The idea this method is based on is quite novel and unexplored, and shows the advantage of being robust to attacks that commonly break some of the existing approaches. Moreover, unlike most of biometric crypto-systems, it doesn't need any error correction code technique. The paper formally discusses about the security of the system by evaluating the probability of an attacker to retrieve the correct cryptographic key, while experimental results show the efficiency and the effectiveness of the binding process by measuring the system performance in terms of accuracy, computational time and storage requirements.
{"title":"Biometric/Cryptographic Keys Binding Based on Function Minimization","authors":"D. Riccio, Clemente Galdi, R. Manzo","doi":"10.1109/SITIS.2016.31","DOIUrl":"https://doi.org/10.1109/SITIS.2016.31","url":null,"abstract":"Traditional cryptosystems are based on the possession of secret keys that can be stolen or shared by non legitimate users. On the other hand, binding the real identity of a system user to what he is instead of something he knows or possesses is the main desirable property of biometric systems. Biometric Crypto-Systems (or BCSs) are designed to bind a cryptographic key with a biometric template to ensure that only a legitimate user can access to encrypted data. In this paper, we propose a new biometric cryptosystem that reformulates the binding process as a minimization problem in such a way that the cryptographic key cannot be derived from the parameters of the objective functions unless the system is provided with a valid biometric template. The idea this method is based on is quite novel and unexplored, and shows the advantage of being robust to attacks that commonly break some of the existing approaches. Moreover, unlike most of biometric crypto-systems, it doesn't need any error correction code technique. The paper formally discusses about the security of the system by evaluating the probability of an attacker to retrieve the correct cryptographic key, while experimental results show the efficiency and the effectiveness of the binding process by measuring the system performance in terms of accuracy, computational time and storage requirements.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128963158","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}
Emanuele Damiano, Raffaele Spinelli, M. Esposito, G. Pietro
In the last years, Cognitive Systems are increasingly appearing, offering new ways for developing Question Answering solutions able to autonomously extract an answer for a question formulated in natural language. Currently, to the best of our knowledge, most of the available Question Answering solutions are designed for the English language and use SQL-like knowledge bases to provide factual answers to a natural language question. Starting from these considerations, this work presents a preliminary Question Answering framework for closed-domains, like Cultural Heritage. It has been expressly thought to extract factual answers from collections of documents by operating with the Italian language. Such a framework exploits a variety of NLP methods for the Italian language to help the understanding of user's questions and the extraction of precise answers from textual passages contained into documents. Moreover, Deep Learning techniques have been used to proficiently understand the topic of a question, whereas a rule-based approach relying on dictionaries has been applied for the annotation and indexing of collections of documents in Italian, enabling their usage into a state-of-the-art Information Retrieval engine. An experimental session has also been arranged, showing very promising preliminary results.
{"title":"Towards a Framework for Closed-Domain Question Answering in Italian","authors":"Emanuele Damiano, Raffaele Spinelli, M. Esposito, G. Pietro","doi":"10.1109/SITIS.2016.100","DOIUrl":"https://doi.org/10.1109/SITIS.2016.100","url":null,"abstract":"In the last years, Cognitive Systems are increasingly appearing, offering new ways for developing Question Answering solutions able to autonomously extract an answer for a question formulated in natural language. Currently, to the best of our knowledge, most of the available Question Answering solutions are designed for the English language and use SQL-like knowledge bases to provide factual answers to a natural language question. Starting from these considerations, this work presents a preliminary Question Answering framework for closed-domains, like Cultural Heritage. It has been expressly thought to extract factual answers from collections of documents by operating with the Italian language. Such a framework exploits a variety of NLP methods for the Italian language to help the understanding of user's questions and the extraction of precise answers from textual passages contained into documents. Moreover, Deep Learning techniques have been used to proficiently understand the topic of a question, whereas a rule-based approach relying on dictionaries has been applied for the annotation and indexing of collections of documents in Italian, enabling their usage into a state-of-the-art Information Retrieval engine. An experimental session has also been arranged, showing very promising preliminary results.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117345237","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}
Today it is very important to maintain an intermediary level of security to ensure safe and trusted communication for daily usage. Secured data communication over internet and any other network is hard to achieve due to the threat of intrusions and misuse. Unfortunately, none of the existing systems have proved to be flawless, though various approaches have been used to thwart network intrusion activities. This paper proposes an investigation Model for Identifying and Collecting Intrusion Evidences (MICIE). In particular, the proposed model MICIE comprises three main features, SNORT as IDS, MySQL as database and BASE for result viewing. These features were installed on Raspberry Pi, which was used to aid the data collection process. The results demonstrated that the proposed model is promising for identifying and collecting evidence of network intrusions in real time.
{"title":"MICIE: A Model for Identifying and Collecting Intrusion Evidences","authors":"Abdulghani Ali, Yee Wai Kit","doi":"10.1109/SITIS.2016.54","DOIUrl":"https://doi.org/10.1109/SITIS.2016.54","url":null,"abstract":"Today it is very important to maintain an intermediary level of security to ensure safe and trusted communication for daily usage. Secured data communication over internet and any other network is hard to achieve due to the threat of intrusions and misuse. Unfortunately, none of the existing systems have proved to be flawless, though various approaches have been used to thwart network intrusion activities. This paper proposes an investigation Model for Identifying and Collecting Intrusion Evidences (MICIE). In particular, the proposed model MICIE comprises three main features, SNORT as IDS, MySQL as database and BASE for result viewing. These features were installed on Raspberry Pi, which was used to aid the data collection process. The results demonstrated that the proposed model is promising for identifying and collecting evidence of network intrusions in real time.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114393875","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}
Michalis Papakostas, Theodoros Giannakopoulos, F. Makedon, V. Karkaletsis
This paper proposes a deep learning classification method for frame-wise recognition of human activities, using raw color (RGB) information. In particular, we present a Convolutional Neural Network (CNN) classification approach for recognising three basic motion activity classes, that cover the vast majority of human activities in the context of a home monitoring environment, namely: sitting, walking and standing up. A real-world fully annotated dataset has been compiled, in the context of an assisted living home environment. Through extensive experimentation we have highlighted the benefits of deep learning architectures against traditional shallow classifiers functioning on hand-crafted features, on the task of activity recognition. Our approach proves the robustness and the quality of CNN classifiers that lies on learning highly invariant features. Our ultimate goal is to tackle the challenging task of activity recognition in environments that are characterized with high levels of inherent noise.
{"title":"Short-Term Recognition of Human Activities Using Convolutional Neural Networks","authors":"Michalis Papakostas, Theodoros Giannakopoulos, F. Makedon, V. Karkaletsis","doi":"10.1109/SITIS.2016.56","DOIUrl":"https://doi.org/10.1109/SITIS.2016.56","url":null,"abstract":"This paper proposes a deep learning classification method for frame-wise recognition of human activities, using raw color (RGB) information. In particular, we present a Convolutional Neural Network (CNN) classification approach for recognising three basic motion activity classes, that cover the vast majority of human activities in the context of a home monitoring environment, namely: sitting, walking and standing up. A real-world fully annotated dataset has been compiled, in the context of an assisted living home environment. Through extensive experimentation we have highlighted the benefits of deep learning architectures against traditional shallow classifiers functioning on hand-crafted features, on the task of activity recognition. Our approach proves the robustness and the quality of CNN classifiers that lies on learning highly invariant features. Our ultimate goal is to tackle the challenging task of activity recognition in environments that are characterized with high levels of inherent noise.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172154","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}
A new technique for color quantization is suggested. First, pre-quantization is accomplished by means of spatial resolution reduction, then, color aggregation is accomplished based on the distance between colors in the color space. Color aggregation is an iterated process where the number of iterations is given by the difference between the number of colors of the pre-quantized image, and the number of colors desired for the quantized image. Color mapping is finally accomplished. Performance evaluation is done in terms of generally adopted quality measures. Comparisons with other methods in the literature are also provided.
{"title":"A New Method for Color Quantization","authors":"G. Ramella, G. S. D. Baja","doi":"10.1109/SITIS.2016.10","DOIUrl":"https://doi.org/10.1109/SITIS.2016.10","url":null,"abstract":"A new technique for color quantization is suggested. First, pre-quantization is accomplished by means of spatial resolution reduction, then, color aggregation is accomplished based on the distance between colors in the color space. Color aggregation is an iterated process where the number of iterations is given by the difference between the number of colors of the pre-quantized image, and the number of colors desired for the quantized image. Color mapping is finally accomplished. Performance evaluation is done in terms of generally adopted quality measures. Comparisons with other methods in the literature are also provided.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176528","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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