Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949444
Thomas Ferrari, G. Gini, N. G. Bakhtyari, E. Benfenati
Encouraged by recent legislations all over the world, aimed to protect human health and environment, in silico techniques have proved their ability to assess the toxicity of chemicals. However, they act often like a black-box, without giving a clear contribution to the scientific insight; such over-optimized methods may be beyond understanding, behaving more like competitors of human experts' knowledge, rather than assistants. In this work, a new Structure-Activity Relationship (SAR) approach is proposed to mine molecular fragments that act like structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make its predictions more reliable, but also to enable a clear control by the user, in order to match customized requirements. Such an approach has been implemented and tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, discovering much of the knowledge already collected in literature as well as new evidences. The achieved tool is a powerful instrument for both SAR knowledge discovery and for activity prediction on untested compounds.
{"title":"Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships","authors":"Thomas Ferrari, G. Gini, N. G. Bakhtyari, E. Benfenati","doi":"10.1109/CIDM.2011.5949444","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949444","url":null,"abstract":"Encouraged by recent legislations all over the world, aimed to protect human health and environment, in silico techniques have proved their ability to assess the toxicity of chemicals. However, they act often like a black-box, without giving a clear contribution to the scientific insight; such over-optimized methods may be beyond understanding, behaving more like competitors of human experts' knowledge, rather than assistants. In this work, a new Structure-Activity Relationship (SAR) approach is proposed to mine molecular fragments that act like structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make its predictions more reliable, but also to enable a clear control by the user, in order to match customized requirements. Such an approach has been implemented and tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, discovering much of the knowledge already collected in literature as well as new evidences. The achieved tool is a powerful instrument for both SAR knowledge discovery and for activity prediction on untested compounds.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129431837","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949428
Jochen De Weerdt, M. D. Backer, J. Vanthienen, B. Baesens
Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a novel approach for evaluating discovered process models based on artificially generated negative events. This approach allows for the definition of a behavioral F-measure for discovered process models, which is the main contribution of this paper.
{"title":"A robust F-measure for evaluating discovered process models","authors":"Jochen De Weerdt, M. D. Backer, J. Vanthienen, B. Baesens","doi":"10.1109/CIDM.2011.5949428","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949428","url":null,"abstract":"Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a novel approach for evaluating discovered process models based on artificially generated negative events. This approach allows for the definition of a behavioral F-measure for discovered process models, which is the main contribution of this paper.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121069545","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949446
P. Blanchart, Marin Ferecatu, M. Datcu
In the context of image search and classification, we describe an active learning strategy that relies on the intrinsic data distribution modeled as a mixture of Gaussians to speed up the learning of the target class using an interactive relevance feedback process. The contributions of our work are twofold: First, we introduce a new form of a semi-supervised C-SVM algorithm that exploits the intrinsic data distribution by working directly on equiprobable envelopes of Gaussian mixture components. Second, we introduce an active learning strategy which allows to interactively adjust the equiprobable envelopes in a small number of feedback steps. The proposed method allows the exploitation of the information contained in the unlabeled data and does not suffer from the drawbacks inherent to semi-supervised methods, e.g. computation time and memory requirements. Tests performed on a database of high-resolution satellite images and on a database of color images show that our system compares favorably, in terms of learning speed and ability to manage large volumes of data, to the classic approach using SVM active learning.
{"title":"Active learning using the data distribution for interactive image classification and retrieval","authors":"P. Blanchart, Marin Ferecatu, M. Datcu","doi":"10.1109/CIDM.2011.5949446","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949446","url":null,"abstract":"In the context of image search and classification, we describe an active learning strategy that relies on the intrinsic data distribution modeled as a mixture of Gaussians to speed up the learning of the target class using an interactive relevance feedback process. The contributions of our work are twofold: First, we introduce a new form of a semi-supervised C-SVM algorithm that exploits the intrinsic data distribution by working directly on equiprobable envelopes of Gaussian mixture components. Second, we introduce an active learning strategy which allows to interactively adjust the equiprobable envelopes in a small number of feedback steps. The proposed method allows the exploitation of the information contained in the unlabeled data and does not suffer from the drawbacks inherent to semi-supervised methods, e.g. computation time and memory requirements. Tests performed on a database of high-resolution satellite images and on a database of color images show that our system compares favorably, in terms of learning speed and ability to manage large volumes of data, to the classic approach using SVM active learning.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016308","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949296
S. Changuel, Nicolas Labroche
The objective of any tutoring system is to provide meaningful learning to the learner, thence it is important to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. In this paper, we study the problem of identifying defined concepts and prerequisite concepts from learning resources available on the web. Statistics and machine learning tools are exploited in order to predict the class of each concept. Two groups of features are constructed to categorise the concepts: contextual features and local features. The contextual features enclose linguistic information and the local features contain the concept properties such as font size and font weigh. An aggregation method is proposed as a solution to the problem of the multiple occurrences of a defined concept in a document. This paper shows that best results are obtained with the SVM classifier than with other classifiers.
{"title":"Distinguishing defined concepts from prerequisite concepts in learning resources","authors":"S. Changuel, Nicolas Labroche","doi":"10.1109/CIDM.2011.5949296","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949296","url":null,"abstract":"The objective of any tutoring system is to provide meaningful learning to the learner, thence it is important to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. In this paper, we study the problem of identifying defined concepts and prerequisite concepts from learning resources available on the web. Statistics and machine learning tools are exploited in order to predict the class of each concept. Two groups of features are constructed to categorise the concepts: contextual features and local features. The contextual features enclose linguistic information and the local features contain the concept properties such as font size and font weigh. An aggregation method is proposed as a solution to the problem of the multiple occurrences of a defined concept in a document. This paper shows that best results are obtained with the SVM classifier than with other classifiers.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127547564","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949454
E. Nosova, G. Raiconi, R. Tagliaferri
In the last years a large amount of information about genomes was discovered, increasing the complexity of analysis. Therefore the most advanced techniques and algorithms are required. In many cases researchers use unsupervised clustering. But the inability of clustering to solve a number of tasks requires new algorithms. So, recently, scientists turned their attention to the biclustering techniques. In this paper we propose a novel biclustering technique, that we call Combinatorial Biclustering Algorithm (BCA). This technique permits to solve the following problems: 1) classification of data with respect to rows and columns together; 2) discovering of the overlapped biclusters; 3) definition of the minimal number of rows and columns in biclusters; 4) finding all biclusters together. We apply our model to two synthetic and one real biological data sets and show the results.
{"title":"A multi-Biclustering Combinatorial Based algorithm","authors":"E. Nosova, G. Raiconi, R. Tagliaferri","doi":"10.1109/CIDM.2011.5949454","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949454","url":null,"abstract":"In the last years a large amount of information about genomes was discovered, increasing the complexity of analysis. Therefore the most advanced techniques and algorithms are required. In many cases researchers use unsupervised clustering. But the inability of clustering to solve a number of tasks requires new algorithms. So, recently, scientists turned their attention to the biclustering techniques. In this paper we propose a novel biclustering technique, that we call Combinatorial Biclustering Algorithm (BCA). This technique permits to solve the following problems: 1) classification of data with respect to rows and columns together; 2) discovering of the overlapped biclusters; 3) definition of the minimal number of rows and columns in biclusters; 4) finding all biclusters together. We apply our model to two synthetic and one real biological data sets and show the results.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994229","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949443
K. Bunte, Michael Biehl, B. Hammer
A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives. Most methods, however, provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based on a simple global linear mapping as well as prototype-based local linear mappings.
{"title":"Dimensionality reduction mappings","authors":"K. Bunte, Michael Biehl, B. Hammer","doi":"10.1109/CIDM.2011.5949443","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949443","url":null,"abstract":"A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives. Most methods, however, provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based on a simple global linear mapping as well as prototype-based local linear mappings.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115849749","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949430
Pekka Siirtola, P. Laurinen, J. Röning, H. Kinnunen
The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.
{"title":"Efficient accelerometer-based swimming exercise tracking","authors":"Pekka Siirtola, P. Laurinen, J. Röning, H. Kinnunen","doi":"10.1109/CIDM.2011.5949430","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949430","url":null,"abstract":"The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151134","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949458
Carlos Menendez-Gonzalez, G. Triviño
The amount of data provided by computers about objects in our environment increases. Nevertheless, the ability to extract relevant and understandable knowledge from this information remains limited. On-Line Analytical Processing (OLAP) is a well known paradigm used to help users to navigate by the information stored in databases. In the research line of Computational Theory of Perceptions, we have created the Granular Linguistic Model of a Phenomenon. It is a data structure that allows computational systems to generate linguistic descriptions of input data. In this paper, we explore the possibilities of using OLAP to navigate in this structure of information. Inspired in the way humans use NL, we adapt the typical operations in OLAP, namely, drilling, rolling and slicing/dicing, to navigate by hierarchical granular structures of fuzzy perceptions. The long term aim is to create a new type of human computer interface that will assist users in the analysis of the likely huge amount of available information about relevant phenomena. Obtained results show the viability of this approach including a practical demonstration of concept.
{"title":"OLAP navigation in the Granular Linguistic Model of a Phenomenon","authors":"Carlos Menendez-Gonzalez, G. Triviño","doi":"10.1109/CIDM.2011.5949458","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949458","url":null,"abstract":"The amount of data provided by computers about objects in our environment increases. Nevertheless, the ability to extract relevant and understandable knowledge from this information remains limited. On-Line Analytical Processing (OLAP) is a well known paradigm used to help users to navigate by the information stored in databases. In the research line of Computational Theory of Perceptions, we have created the Granular Linguistic Model of a Phenomenon. It is a data structure that allows computational systems to generate linguistic descriptions of input data. In this paper, we explore the possibilities of using OLAP to navigate in this structure of information. Inspired in the way humans use NL, we adapt the typical operations in OLAP, namely, drilling, rolling and slicing/dicing, to navigate by hierarchical granular structures of fuzzy perceptions. The long term aim is to create a new type of human computer interface that will assist users in the analysis of the likely huge amount of available information about relevant phenomena. Obtained results show the viability of this approach including a practical demonstration of concept.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131603066","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949306
Sheng Gao, Ludovic Denoyer, P. Gallinari
We address the problem of link prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While traditional link prediction models are limited to single-type link prediction we attempt here to jointly model and predict the multiple relation types, which we refer to as the Link Pattern Prediction (LPP) problem. For that, we propose a tensor decomposition model to solve the LPP problem, which allows to capture the correlations among different relation types and reveal the impact of various relations on prediction performance. The proposed tensor decomposition model is efficiently learned with a conjugate gradient based optimization method. Extensive experiments on real-world datasets demonstrate that this model outperforms the traditional mono-relational model and can achieve better prediction quality.
{"title":"Link Pattern Prediction with tensor decomposition in multi-relational networks","authors":"Sheng Gao, Ludovic Denoyer, P. Gallinari","doi":"10.1109/CIDM.2011.5949306","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949306","url":null,"abstract":"We address the problem of link prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While traditional link prediction models are limited to single-type link prediction we attempt here to jointly model and predict the multiple relation types, which we refer to as the Link Pattern Prediction (LPP) problem. For that, we propose a tensor decomposition model to solve the LPP problem, which allows to capture the correlations among different relation types and reveal the impact of various relations on prediction performance. The proposed tensor decomposition model is efficiently learned with a conjugate gradient based optimization method. Extensive experiments on real-world datasets demonstrate that this model outperforms the traditional mono-relational model and can achieve better prediction quality.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114532877","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}
Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949298
A. Ciampi, A. Appice, D. Malerba, P. Guccione
In many real-time applications, such as wireless sensor network monitoring, traffic control or health monitoring systems, it is required to analyze continuous and unbounded geographically distributed streams of data (e.g. temperature or humidity measurements transmitted by sensors of weather stations). Storing and querying geo-referenced stream data poses specific challenges both in time (real-time processing) and in space (limited storage capacity). Summarization algorithms can be used to reduce the amount of data to be permanently stored into a data warehouse without losing information for further subsequent analysis. In this paper we present a framework in which data streams are seen as time-varying realizations of stochastic processes. Signal compression techniques, based on transformed domains, are applied and compared with a geometrical segmentation in terms of compression efficiency and accuracy in the subsequent reconstruction.
{"title":"Trend cluster based compression of geographically distributed data streams","authors":"A. Ciampi, A. Appice, D. Malerba, P. Guccione","doi":"10.1109/CIDM.2011.5949298","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949298","url":null,"abstract":"In many real-time applications, such as wireless sensor network monitoring, traffic control or health monitoring systems, it is required to analyze continuous and unbounded geographically distributed streams of data (e.g. temperature or humidity measurements transmitted by sensors of weather stations). Storing and querying geo-referenced stream data poses specific challenges both in time (real-time processing) and in space (limited storage capacity). Summarization algorithms can be used to reduce the amount of data to be permanently stored into a data warehouse without losing information for further subsequent analysis. In this paper we present a framework in which data streams are seen as time-varying realizations of stochastic processes. Signal compression techniques, based on transformed domains, are applied and compared with a geometrical segmentation in terms of compression efficiency and accuracy in the subsequent reconstruction.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127853487","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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