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Author Jordi Roca
Title Constancy and inconstancy in categorical colour perception Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Maria Vanrell;C. Alejandro Parraga
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (up) CIC Approved no
Call Number Admin @ si @ Roc2012 Serial 2893
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Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer
Title Improving Color Constancy by Photometric Edge Weighting Type Journal Article
Year 2012 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 34 Issue 5 Pages 918-929
Keywords
Abstract : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.
Address Los Alamitos; CA; USA;
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 0162-8828 ISBN Medium
Area Expedition Conference
Notes (up) CIC;ISE Approved no
Call Number Admin @ si @ GGW2012 Serial 1850
Permanent link to this record
 
 
Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados
Title Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique Type Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages 243-253
Keywords
Abstract Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes (up) DAG Approved no
Call Number Admin @ si @ LRL2012 Serial 2381
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Author Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados
Title Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques Type Conference Article
Year 2012 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages 149-162
Keywords
Abstract
Address
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 ISBN Medium
Area Expedition Conference CIFED
Notes (up) DAG Approved no
Call Number Admin @ si @ LBR2012 Serial 2382
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke
Title Writer Identification in Old Handwritten Music Scores Type Book Chapter
Year 2012 Publication Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology Abbreviated Journal
Volume Issue Pages 27-63
Keywords
Abstract The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
Address
Corporate Author Thesis
Publisher IGI-Global Place of Publication Editor Copnstantin Papaodysseus
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ FLS2012 Serial 1828
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Author Lluis Gomez
Title Perceptual Organization for Text Extraction in Natural Scenes Type Report
Year 2012 Publication CVC Technical Report Abbreviated Journal
Volume 173 Issue Pages
Keywords
Abstract
Address Bellaterra
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ Gom2012 Serial 2309
Permanent link to this record
 
 
Author Jon Almazan; Alicia Fornes; Ernest Valveny
Title A non-rigid appearance model for shape description and recognition Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR
Volume 45 Issue 9 Pages 3105--3113
Keywords Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition
Abstract In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach.
Address
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 0031-3203 ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number DAG @ dag @ AFV2012 Serial 1982
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Author Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny
Title A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection Type Conference Article
Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 453-458
Keywords
Abstract In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.
Address
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 ISBN 978-1-4673-2262-1 Medium
Area Expedition Conference ICFHR
Notes (up) DAG Approved no
Call Number DAG @ dag @ AFF2012 Serial 1983
Permanent link to this record
 
 
Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title Efficient Exemplar Word Spotting Type Conference Article
Year 2012 Publication 23rd British Machine Vision Conference Abbreviated Journal
Volume Issue Pages 67.1- 67.11
Keywords
Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
Address
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 ISBN 1-901725-46-4 Medium
Area Expedition Conference BMVC
Notes (up) DAG Approved no
Call Number DAG @ dag @ AGF2012 Serial 1984
Permanent link to this record
 
 
Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Graph Embedding in Vector Spaces by Node Attribute Statistics Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR
Volume 45 Issue 9 Pages 3072-3083
Keywords Structural pattern recognition; Graph embedding; Data clustering; Graph classification
Abstract Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.
Address
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 0031-3203 ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ GVB2012a Serial 1992
Permanent link to this record
 
 
Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Feature Selection on Node Statistics Based Embedding of Graphs Type Journal Article
Year 2012 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 15 Pages 1980–1990
Keywords Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification
Abstract Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods.
Address
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 ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ GVB2012b Serial 1993
Permanent link to this record
 
 
Author Sophie Wuerger; Kaida Xiao; Dimitris Mylonas; Q. Huang; Dimosthenis Karatzas; Galina Paramei
Title Blue green color categorization in mandarin english speakers Type Journal Article
Year 2012 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 29 Issue 2 Pages A102-A1207
Keywords
Abstract Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.
Address
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 ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ WXM2012 Serial 2007
Permanent link to this record
 
 
Author Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin
Title Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval Type Journal Article
Year 2012 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 35 Issue 12 Pages 2916-2929
Keywords
Abstract This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.
Address
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 0162-8828 ISBN 978-1-4577-0394-2 Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ GLG 2012b Serial 2008
Permanent link to this record
 
 
Author Albert Gordo; Florent Perronnin; Ernest Valveny
Title Document classification using multiple views Type Conference Article
Year 2012 Publication 10th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 33-37
Keywords
Abstract The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.
Address Australia
Corporate Author Thesis
Publisher IEEE Computer Society Washington Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-7695-4661-2 Medium
Area Expedition Conference DAS
Notes (up) DAG Approved no
Call Number Admin @ si @ GPV2012 Serial 2049
Permanent link to this record
 
 
Author Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny
Title Leveraging category-level labels for instance-level image retrieval Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 3045-3052
Keywords
Abstract In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.
Address Providence, Rhode Island
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium
Area Expedition Conference CVPR
Notes (up) DAG Approved no
Call Number Admin @ si @ GRP2012 Serial 2050
Permanent link to this record