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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Factorization with Missing and Noisy Data Type Conference Article
Year 2006 Publication 6th International Conference on Computational Science Abbreviated Journal ICCS´06
Volume LNCS 3991 Issue Pages 555–562
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Abstract
Address Reading (United Kingdom)
Corporate Author Thesis
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ISSN (up) ISBN Medium
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2006b Serial 653
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Iterative Multiresolution Scheme for SFM Type Conference Article
Year 2006 Publication International Conference on Image Analysis and Recognition Abbreviated Journal ICIAR 2006
Volume LNCS 4141 Issue 1 Pages 804–815
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Address
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2006c Serial 704
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Motion Segmentation from Feature Trajectories with Missing Data Type Conference Article
Year 2007 Publication 3rd. Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal IbPRIA 2007
Volume LNCS 4477 Issue Pages 483–490
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Abstract
Address Girona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor J. Marti et al. (Eds.)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2007a Serial 814
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Rank Estimation in 3D Multibody Motion Segmentation Type Journal Article
Year 2008 Publication Electronic Letters Abbreviated Journal
Volume 44 Issue 4 Pages 279-280
Keywords
Abstract A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data.
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Series Editor Series Title Abbreviated Series Title
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ISSN (up) ISBN Medium
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008a Serial 939
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Adapted Alternation Approach for Recommender Systems Type Conference Article
Year 2008 Publication IEEE International Conference on e–Business Engineering, Abbreviated Journal
Volume Issue Pages 128–135
Keywords
Abstract This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
Address Xi’an (Xina)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN (up) ISBN Medium
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2008e Serial 1044
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An iterative multiresolution scheme for SFM with missing data Type Journal Article
Year 2009 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 34 Issue 3 Pages 240–258
Keywords
Abstract Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN (up) ISBN Medium
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2009a Serial 1163
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes Type Journal Article
Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 28 Issue 1 Pages 164-176
Keywords
Abstract Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 0262-8856 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2010 Serial 1278
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Rank Estimation in Missing Data Matrix Problems Type Journal Article
Year 2011 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 39 Issue 2 Pages 140-160
Keywords
Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 0924-9907 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ JSL2011; Serial 1710
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach Type Journal Article
Year 2009 Publication International Journal of Electronic Commerce Abbreviated Journal
Volume 14 Issue 1 Pages 89-108
Keywords
Abstract The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1086-4415 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2009b Serial 1237
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