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Author  |
Jaume Amores |
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Title |
Multiple Instance Classification: review, taxonomy and comparative study |
Type |
Journal Article |
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Year |
2013 |
Publication |
Artificial Intelligence |
Abbreviated Journal |
AI |
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Volume |
201 |
Issue |
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Pages |
81-105 |
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Keywords |
Multi-instance learning; Codebook; Bag-of-Words |
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Abstract |
Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods. |
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Elsevier Science Publishers Ltd. Essex, UK |
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0004-3702 |
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ADAS; 601.042; 600.057 |
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no |
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Call Number |
Admin @ si @ Amo2013 |
Serial |
2273 |
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Author |
Jaume Amores |
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Title |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
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Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
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Volume |
42 |
Issue |
2 |
Pages |
381-407 |
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Keywords |
Multi-instance learning; Codebook; Bag of words |
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Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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Springer London |
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0219-1377 |
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ADAS; 601.042; 600.057; 600.076 |
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Admin @ si @ Amo2015 |
Serial |
2383 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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Pages |
4246–4250 |
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Abstract |
Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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ADAS |
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Call Number |
ADAS @ adas @ Amo2010 |
Serial |
1295 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
Type |
Conference Article |
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Year |
2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Keywords |
Multiple Instance Learning; Active Learning; Object-class segmentation. |
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Abstract |
In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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Hong-Kong |
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ICMV |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ AGL2010b |
Serial |
1429 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors |
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Miscellaneous |
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Year |
2005 |
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IEEE Computer Society, International Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(2):769–774 |
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San Diego, CA (USA) |
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MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2005a |
Serial |
541 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Efficient Object-Class Recognition by Boosting Contextual Information |
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Miscellaneous |
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Year |
2005 |
Publication |
Pattern Recognition and Image Analysis, IbPRIA 2005, LNCS 3522:28–35 |
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Estoril (Portugal) |
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ADAS;MILAB |
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Call Number |
ADAS @ adas @ ASR2005b |
Serial |
554 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
Type |
Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
27 |
Issue |
3 |
Pages |
201–209 |
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ADAS;MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2006 |
Serial |
643 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS;MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2007b |
Serial |
922 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Class-Specific Binaryy Correlograms for Object Recognition |
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Conference Article |
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2007 |
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British Machine Vision Conference |
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Warwick (UK) |
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BMVC’07 |
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ADAS;MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2007a |
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923 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva; Theo Gevers; A. Smeulders |
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Title |
Boosting Contextual Information in Content-based Image Retrieval |
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Miscellaneous |
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2004 |
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6th ACM SIGMM International Workshop on Multimedia Information Retrieval |
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New York, USA |
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MILAB;ISE |
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ADAS @ adas @ ASR2004 |
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466 |
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Jaume Amores; Petia Radeva |
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Title |
Non-rigid Registration of Vessel Structures in IVUS Images |
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2003 |
Publication |
In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science. 2652:45–52 |
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Springer-Verlag |
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ADAS;MILAB |
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ADAS @ adas @ AmR2003 |
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363 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Elastic Matching and Retrieval of IVUS Images Using Contextual Information |
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2003 |
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6th Catalan Conference on Artificial Intelligence (CCIA 2003), 137–148 |
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Palma de Mallorca |
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ADAS @ adas @ AmR2003a |
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364 |
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Jaume Amores; Petia Radeva |
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Registration and retrieval of medical images. Application to IVUS |
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2004 |
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CVC Technical Report #77 |
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CVC (UAB) |
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ADAS @ adas @ AmR2004 |
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513 |
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Jaume Amores; Petia Radeva |
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Title |
Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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PRL |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005b |
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592 |
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Jaume Amores; Petia Radeva |
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Title |
Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS @ adas @ AmR2005a |
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