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@inproceedings{Montavont2006,
abstract = {IEEE 802.11 networks are now very common and are present in various locations. While roaming through access points, a mobile node is often required to perform a link layer handover. This mechanism causes user-interceptable connection loss and breaks in time-sensitive communication, especially if a network layer handover follows the link layer handover. Many solutions attempting to improve this process have been proposed but only a few use geolocation systems in the management of the handover. In this article, we present a new method to enhance both link layer and network layer handovers using geolocation information provided by a GPS system. The idea behind our algorithm is to predict the next mobile node point of attachment and the associated sub-network using the position of the mobile nodes. This method has been implemented using the new Mobile IP daemon for GNU/Linux operating system and evaluated through two scenarios},
author = {Montavont, J. and Noel, T.},
booktitle = {IEEE Int. Conf. Wirel. Mob. Comput. Netw. Commun. 2006.},
doi = {10.1109/WIMOB.2006.1696358},
isbn = {1-4244-0494-0},
keywords = {Broadcasting,GNU operating system,GPS system,Global Positioning System,IEEE 802.11 handover,IP networks,Linux,Linux operating system,Mobile communication,Operating systems,Prediction algorithms,Probes,Roaming,Telecommunication traffic,Throughput,WLAN,geolocation information,mobile IP daemon,mobile radio,wireless LAN,wireless local area network},
pages = {166--172},
publisher = {IEEE},
shorttitle = {Wireless and Mobile Computing, Networking and Comm},
title = {{IEEE 802.11 Handovers Assisted by GPS Information}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1696358},
year = {2006}
}
@article{Solem2012,
author = {Solem, Jan Erik},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/ProgrammingComputerVision\_CCdraft.pdf:pdf},
title = {{Programming Computer Vision with Python}},
year = {2012}
}
@article{Bueno2011,
author = {Bueno, LM},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/lucasBueno2001mscDissertation.pdf:pdf},
title = {{An\'{a}lise de descritores locais de imagens no contexto de detecc\~{a}o de semi-r\'{e}plicas}},
url = {http://www.dca.fee.unicamp.br/~dovalle/recod/works/lucasBueno2001mscDissertation.pdf},
year = {2011}
}
@article{Juan2009,
author = {Juan, Luo and Gwun, O},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/IJIP-51.pdf:pdf},
journal = {Int. J. Image Process.},
keywords = {knn,pca-sift,ransac,robust detectors,sift,surf},
number = {4},
pages = {143--152},
title = {{A comparison of sift, pca-sift and surf}},
url = {http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue4/IJIP-51.pdf},
year = {2009}
}
@book{Han2006,
abstract = {Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.},
author = {Han, Jiawei and Kamber, Micheline and Pei, Jian},
isbn = {0080475582},
title = {{Data Mining, Second Edition: Concepts and Techniques}},
year = {2006}
}
@article{Shao2009,
author = {Shao, H and Ji, J and Kang, Y and Zhao, H},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Shao et al. - 2009 - Application Research of Homogeneous Texture Descriptor in Content-Based Image Retrieval.pdf:pdf},
isbn = {9781424449941},
journal = {Inf. Eng. \ldots},
keywords = {-mpeg-7},
number = {2008515},
pages = {2--5},
title = {{Application Research of Homogeneous Texture Descriptor in Content-Based Image Retrieval}},
year = {2009}
}
@book{Forsyth2011,
abstract = {Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods},
author = {Forsyth, David A. and Ponce, Jean},
isbn = {013608592X},
pages = {761},
publisher = {Pearson Education, Limited},
title = {{Computer Vision: A Modern Approach}},
year = {2011}
}
@article{Cai2004,
address = {New York, New York, USA},
author = {Cai, Deng and He, Xiaofei and Li, Zhiwei and Ma, Wei-Ying and Wen, Ji-Rong},
doi = {10.1145/1027527.1027747},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Cai et al. - 2004 - Hierarchical clustering of WWW image search results using visual, textual and link information.pdf:pdf},
isbn = {1581138938},
journal = {Proc. 12th Annu. ACM Int. Conf. Multimed. - Multimed. '04},
publisher = {ACM Press},
title = {{Hierarchical clustering of WWW image search results using visual, textual and link information}},
url = {http://portal.acm.org/citation.cfm?doid=1027527.1027747},
year = {2004}
}
@article{Christopoulos2000,
author = {Christopoulos, Charilaos and Berg, Daniel and Skodras, Athanassios},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Christopoulos, Berg, Skodras - 2000 - The colour in the upcoming MPEG-7 standard.pdf:pdf},
journal = {Invit. Pap. Eur. \ldots},
keywords = {colour spaces,content-based,feature histogram,image retrieval,mpeg-7,search},
pages = {1--4},
title = {{The colour in the upcoming MPEG-7 standard}},
year = {2000}
}
@article{Sivic2003,
author = {Sivic, Josef and Zisserman, Andrew},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Sivic, Zisserman - 2003 - Video Google A text retrieval approach to object matching in videos.pdf:pdf},
isbn = {0769519504},
journal = {Comput. Vision, 2003. Proceedings. \ldots},
number = {Iccv},
pages = {2--9},
title = {{Video Google: A text retrieval approach to object matching in videos}},
year = {2003}
}
@book{Russell2011,
abstract = {Popular social networks such as Facebook and Twitter generate a tremendous amount of valuable data on topics and use patterns. Whos talking to whom? What are they talking about? How often are they talking? This concise and practical book shows you how to answer these questions and more by harvesting and analyzing data using social web APIs, Python tools, GitHub, HTML5, and JavaScript.},
author = {Russell, Matthew A},
booktitle = {Book},
doi = {10.1081/E-ELIS3-120043522},
isbn = {9781449388348},
issn = {15280691},
keywords = {Basic,Collective Intelligence,ML,NLP,SNS,Social Web,book,data mining,data visualization,facebook,social network,social network analysis,twitter},
mendeley-tags = {Basic,Collective Intelligence,ML,NLP,SNS,Social Web,book,data mining,data visualization,facebook,social network,social network analysis,twitter},
pages = {5623--5633},
pmid = {21630419},
title = {{Mining the Social Web}},
volume = {54},
year = {2011}
}
@article{Sivic2006,
abstract = {We describe an approach to object retrieval which searches for and localizes all the occurrences of an object in a video, given a query image of the object. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject those that are unstable. Efficient retrieval is achieved by employing methods from statistical text retrieval, including inverted file systems, and text and document frequency weightings. This requires a visual analogy of a word which is provided here by vector quantizing the region descriptors. The final ranking also depends on the spatial layout of the regions. The result is that retrieval is immediate, returning a ranked list of shots in the manner of Google. We report results for object retrieval on the full length feature films ‘Groundhog Day’ and ‘Casablanca’.},
author = {Sivic, Josef and Zisserman, Andrew},
doi = {10.1007/11957959\_7},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Sivic, Zisserman - 2006 - Video Google Efficient visual search of videos.pdf:pdf},
isbn = {978-3-540-68794-8},
journal = {Towar. Categ. Object Recognit.},
pages = {127--144},
title = {{Video Google: Efficient visual search of videos}},
volume = {4170},
year = {2006}
}
@article{Modi2008,
author = {Modi, Vinay},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Modi - 2008 - Color descriptors from compressed images.pdf:pdf},
title = {{Color descriptors from compressed images}},
year = {2008}
}
@article{Lowe1999,
author = {Lowe, D.G.},
doi = {10.1109/ICCV.1999.790410},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Lowe - 1999 - Object recognition from local scale-invariant features.pdf:pdf},
isbn = {0-7695-0164-8},
journal = {Proc. Seventh IEEE Int. Conf. Comput. Vis.},
pages = {1150--1157 vol.2},
publisher = {Ieee},
title = {{Object recognition from local scale-invariant features}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=790410},
year = {1999}
}
@book{Liu2011,
abstract = {Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.},
author = {Liu, Bing},
isbn = {3642194605},
pages = {644},
publisher = {Springer},
title = {{Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data}},
year = {2011}
}
@article{Lin2014,
abstract = {In image retrieval, the image feature is the main factor determining accuracy; the color feature is the most important feature and is most commonly used with a K-means algorithm. To create a fast K-means algorithm for this study, first a level histogram of statistics for the image database is made. The level histogram is used with the K-means algorithm for clustering data. A fast K-means algorithm not only shortens the length of time spent on training the image database cluster centers, but it also overcomes the cluster center re-training problem since large numbers of images are continuously added into the database. For the experiment, we use gray and color image database sets for performance comparisons and analyzes, respectively. The results show that the fast K-means algorithm is more effective, faster, and more convenient than the traditional K-means algorithm. Moreover, it overcomes the problem of spending excessive amounts of time on re-training caused by the continuous addition of images to the image database. Selection of initial cluster centers also affects the performance of cluster center training.},
author = {Lin, Chuen-Horng and Chen, Chun-Chieh and Lee, Hsin-Lun and Liao, Jan-Ray},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Lin et al. - 2014 - Fast K-means algorithm based on a level histogram for image retrieval.pdf:pdf},
journal = {Expert Syst. Appl.},
keywords = {Color feature,Histogram,Image retrieval,K-means},
number = {7},
pages = {3276--3283},
title = {{Fast K-means algorithm based on a level histogram for image retrieval}},
url = {http://www.sciencedirect.com/science/article/pii/S0957417413009299},
volume = {41},
year = {2014}
}
@article{Ite-vil,
author = {Ite-vil, Leszek Cieplinski (mitsubishi Electric},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Ohm, Cieplinski, Kim - 2003 - The MPEG-7 Color Descriptors.pdf:pdf},
title = {{The MPEG-7 Color Descriptors Jens-Rainer Ohm (RWTH Aachen, Institute of Communications Engineering)}}
}
@article{Boanjak2012,
author = {Boanjak, M and Oliveira, Eduardo},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Boanjak, Oliveira - 2012 - TwitterEcho a distributed focused crawler to support open research with twitter data(2).pdf:pdf},
isbn = {9781450312301},
journal = {Proc. 21st \ldots},
title = {{TwitterEcho: a distributed focused crawler to support open research with twitter data}},
year = {2012}
}
@article{Lowe2004,
author = {Lowe, David G.},
doi = {10.1023/B:VISI.0000029664.99615.94},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/sift.pdf:pdf},
issn = {0920-5691},
journal = {Int. J. Comput. Vis.},
month = nov,
number = {2},
pages = {91--110},
title = {{Distinctive Image Features from Scale-Invariant Keypoints}},
url = {http://link.springer.com/10.1023/B:VISI.0000029664.99615.94},
volume = {60},
year = {2004}
}
@phdthesis{Cunha2013,
author = {Cunha, TDS},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Cunha - 2013 - TweeProfiles detection of spatio-temporal patterns on Twitter.pdf:pdf},
school = {Faculdade de Engeneharia da Universidade do Porto},
title = {{TweeProfiles: detection of spatio-temporal patterns on Twitter}},
url = {http://paginas.fe.up.pt/~ei08142/files/mieic\_en.pdf},
year = {2013}
}
@article{Cieplinski2001,
author = {Cieplinski, Leszek},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Cieplinski - 2001 - MPEG-7 Color Descriptors and Their Applications.pdf:pdf},
keywords = {color descriptor,image indexing,mpeg-7},
pages = {11--20},
title = {{MPEG-7 Color Descriptors and Their Applications}},
volume = {7},
year = {2001}
}
@article{Fayyad1996,
author = {Fayyad, Usama and Piatetsky-shapiro, Gregory and Smyth, Padhraic},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Fayyad, Piatetsky-shapiro, Smyth - 1996 - From Data Mining to Knowledge Discovery in Databases.pdf:pdf},
pages = {37--54},
title = {{From Data Mining to Knowledge Discovery in Databases}},
year = {1996}
}
@article{Bay2006,
author = {Bay, Herbert and Tuytelaars, Tinne and Gool, Luc Van},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/surf.pdf:pdf},
journal = {Comput. Vision–ECCV 2006},
title = {{Surf: Speeded up robust features}},
url = {http://link.springer.com/chapter/10.1007/11744023\_32},
year = {2006}
}
@article{Bober2001,
author = {Bober, M.},
doi = {10.1109/76.927426},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Bober - 2001 - MPEG-7 visual shape descriptors.pdf:pdf},
issn = {10518215},
journal = {IEEE Trans. Circuits Syst. Video Technol.},
month = jun,
number = {6},
pages = {716--719},
title = {{MPEG-7 visual shape descriptors}},
volume = {11},
year = {2001}
}
@book{Gauman2010,
abstract = {The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions},
author = {Gauman, Kristen and Leibe, Bastian},
isbn = {1598299697},
pages = {181},
publisher = {Morgan \& Claypool Publishers},
title = {{Visual Object Recognition}},
url = {http://books.google.com/books?id=fYZgAQAAQBAJ\&pgis=1},
year = {2010}
}
@inproceedings{Gao2005,
address = {New York, New York, USA},
author = {Gao, Bin and Liu, Tie-Yan and Qin, Tao and Zheng, Xin and Cheng, Qian-Sheng and Ma, Wei-Ying},
booktitle = {Proc. 13th Annu. ACM Int. Conf. Multimed. - Multimed. '05},
doi = {10.1145/1101149.1101167},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Gao et al. - 2005 - Web image clustering by consistent utilization of visual features and surrounding texts.pdf:pdf},
isbn = {1595930442},
keywords = {co-clustering,consistency,image processing,spectral graph},
month = nov,
pages = {112},
publisher = {ACM Press},
title = {{Web image clustering by consistent utilization of visual features and surrounding texts}},
url = {http://dl.acm.org/citation.cfm?id=1101149.1101167},
year = {2005}
}
@article{Wang1997,
author = {Wang, Wei and Yang, Jiong and Muntz, Richard R.},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Wang, Yang, Muntz - 1997 - STING A Statistical Information Grid Approach to Spatial Data Mining.pdf:pdf},
isbn = {1-55860-470-7},
month = aug,
pages = {186--195},
publisher = {Morgan Kaufmann Publishers Inc.},
title = {{STING: A Statistical Information Grid Approach to Spatial Data Mining}},
year = {1997}
}
@article{Wu2001,
author = {Wu, Peng and Ro, YM and Won, CS and Choi, Yanglim},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Wu et al. - 2001 - Texture descriptors in MPEG-7.pdf:pdf},
journal = {Comput. Anal. Images \ldots},
keywords = {mpeg-7,texture descriptor},
pages = {21--28},
title = {{Texture descriptors in MPEG-7}},
year = {2001}
}
@book{North2012,
author = {North, Dr. Matthew A},
isbn = {0615684378},
pages = {264},
publisher = {Global Text Project},
title = {{Data Mining for the Masses}},
year = {2012}
}
@book{Baeza-Yates1999,
author = {Baeza-Yates, R and Ribeiro-Neto, B},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Baeza-Yates, Ribeiro-Neto - 1999 - Modern information retrieval.pdf:pdf},
isbn = {020139829X},
title = {{Modern information retrieval}},
url = {ftp://mail.im.tku.edu.tw/seke/slide/baeza-yates/chap10\_user\_interfaces\_and\_visualization-modern\_ir.pdf},
year = {1999}
}
@book{Nixon2002,
abstract = {Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.*Ideal module text for courses in artificial intelligence, image processing and computer vision*Essential reading for engineers and academics working in this cutting-edge field*Supported by free software on a companion website},
author = {Nixon, Mark S. and Aguado, Alberto S.},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Unknown - Unknown - and.pdf:pdf},
isbn = {0750650788},
title = {{Feature Extraction and Image Processing}},
year = {2002}
}
@article{Manjunath2001,
author = {Manjunath, BS and Ohm, JR},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Manjunath, Ohm - 2001 - Color and texture descriptors.pdf:pdf},
journal = {Circuits Syst. \ldots},
number = {6},
pages = {703--715},
title = {{Color and texture descriptors}},
volume = {11},
year = {2001}
}
@book{Bramer2007,
abstract = {Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This should prove of value to readers of all kinds, from those whose only use of data mining techniques will be via commercial packages right through to academic researchers. This book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.},
author = {Bramer, Max},
isbn = {1846287669},
title = {{Principles of Data Mining}},
year = {2007}
}
@article{Agrawal1998,
author = {Agrawal, Rakesh and Gehrke, Johannes and Gunopulos, Dimitrios and Raghavan, Prabhakar},
doi = {10.1145/276305.276314},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Agrawal et al. - 1998 - Automatic subspace clustering of high dimensional data for data mining applications.pdf:pdf},
isbn = {0-89791-995-5},
issn = {01635808},
journal = {ACM SIGMOD Rec.},
month = jun,
number = {2},
pages = {94--105},
publisher = {ACM},
title = {{Automatic subspace clustering of high dimensional data for data mining applications}},
volume = {27},
year = {1998}
}
@article{Nister2006,
abstract = {A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD\&\#146;s. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.},
author = {Nister, D. and Stewenius, H.},
file = {:Users/ivomota/Dropbox/FEUP/FEUP\_13.14/Disserta\c{c}\~{a}o/Artigos/Nister, Stewenius - 2006 - Scalable recognition with a vocabulary tree.pdf:pdf},
isbn = {0769525970},
journal = {\ldots Vis. Pattern Recognition, 2006 \ldots},
title = {{Scalable recognition with a vocabulary tree}},
url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1641018},
volume = {2},
year = {2006}
}
@article{Pak2010,
author = {Pak, Alexander and Paroubek, Patrick},
journal = {LREC},
pages = {1320--1326},
title = {{Twitter as a Corpus for Sentiment Analysis and Opinion Mining.}},
url = {http://incc-tps.googlecode.com/svn/trunk/TPFinal/bibliografia/Pak and Paroubek (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining.pdf},
year = {2010}
}
@inproceedings{Li2008,
address = {New York, New York, USA},
author = {Li, Xirong and Snoek, Cees G.M. and Worring, Marcel},
booktitle = {Proceeding 1st ACM Int. Conf. Multimed. Inf. Retr. - MIR '08},
doi = {10.1145/1460096.1460126},
isbn = {9781605583129},
keywords = {neighbor voting,social image retrieval,tag relevance},
month = oct,
pages = {180},
publisher = {ACM Press},
title = {{Learning tag relevance by neighbor voting for social image retrieval}},
url = {http://dl.acm.org/citation.cfm?id=1460096.1460126},
year = {2008}
}
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journal = {Comput. Vis. (ICCV \ldots},
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publisher = {Ieee},
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}
@misc{mysql,
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@misc{postgresql,
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@misc{twitter,
 Author = {Twitter, Inc},
 Title = {Twitter},
 Howpublished = {\url{https://twitter.com/}},
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@misc{instagram,
 Author = {Facebook, Inc},
 Title = {Instagram},
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@misc{facebook,
 Author = {Facebook, Inc},
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@misc{twitpic,
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}
@misc{linkedin,
 Title = {LinkedIn},
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@misc{mongodb,
 Author = {MongoDB, Inc},
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@misc{flickr,
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@misc{json,
 Title = {JSON - JsvaScript Object Notation},
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}