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Author Nuria Cirera; Alicia Fornes; Volkmar Frinken; Josep Llados
Title Hybrid grammar language model for handwritten historical documents recognition Type Conference Article
Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume (down) 7887 Issue Pages 117-124
Keywords
Abstract In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate.
Address Madeira; Portugal; June 2013
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 978-3-642-38627-5 Medium
Area Expedition Conference IbPRIA
Notes DAG; 602.006; 600.045; 600.061 Approved no
Call Number Admin @ si @ CFF2013 Serial 2292
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Author Daniel Sanchez; J.C.Ortega; Miguel Angel Bautista
Title Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization Type Conference Article
Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume (down) 7887 Issue Pages 50-58
Keywords Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
Abstract Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.
Address Madeira; Portugal; June 2013
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 978-3-642-38627-5 Medium
Area Expedition Conference IbPRIA
Notes HUPBA Approved no
Call Number SOB2013 Serial 2250
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers Type Conference Article
Year 2013 Publication 26th Canadian Conference on Artificial Intelligence Abbreviated Journal
Volume (down) 7884 Issue Pages 1-12
Keywords Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature
Abstract Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
Address Canada; May 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-38456-1 Medium
Area Expedition Conference AI
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2013b Serial 2249
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Author Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke
Title A Fast Matching Algorithm for Graph-Based Handwriting Recognition Type Conference Article
Year 2013 Publication 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition Abbreviated Journal
Volume (down) 7877 Issue Pages 194-203
Keywords
Abstract The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
Address Vienna; Austria; May 2013
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 978-3-642-38220-8 Medium
Area Expedition Conference GBR
Notes DAG; 600.045; 605.203 Approved no
Call Number Admin @ si @ FSF2013 Serial 2294
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Author Joost Van de Weijer; Fahad Shahbaz Khan
Title Fusing Color and Shape for Bag-of-Words Based Object Recognition Type Conference Article
Year 2013 Publication 4th Computational Color Imaging Workshop Abbreviated Journal
Volume (down) 7786 Issue Pages 25-34
Keywords Object Recognition; color features; bag-of-words; image classification
Abstract In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research.
Address Chiba; Japan; March 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-36699-4 Medium
Area Expedition Conference CCIW
Notes CIC; 600.048 Approved no
Call Number Admin @ si @ WeK2013 Serial 2283
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Author Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny
Title Notation-invariant patch-based wall detector in architectural floor plans Type Book Chapter
Year 2013 Publication Graphics Recognition. New Trends and Challenges Abbreviated Journal
Volume (down) 7423 Issue Pages 79--88
Keywords
Abstract Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper.
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 978-3-642-36823-3 Medium
Area Expedition Conference
Notes DAG; 600.045; 600.056; 605.203 Approved no
Call Number Admin @ si @ HMS2013 Serial 2322
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Author Alvaro Cepero; Albert Clapes; Sergio Escalera
Title Quantitative analysis of non-verbal communication for competence analysis Type Conference Article
Year 2013 Publication 16th Catalan Conference on Artificial Intelligence Abbreviated Journal
Volume (down) 256 Issue Pages 105-114
Keywords
Abstract
Address Vic; October 2013
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 CCIA
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ CCE2013 Serial 2324
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Author Jaume Amores
Title Multiple Instance Classification: review, taxonomy and comparative study Type Journal Article
Year 2013 Publication Artificial Intelligence Abbreviated Journal AI
Volume (down) 201 Issue Pages 81-105
Keywords Multi-instance learning; Codebook; Bag-of-Words
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.
Address
Corporate Author Thesis
Publisher Elsevier Science Publishers Ltd. Essex, UK Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-3702 ISBN Medium
Area Expedition Conference
Notes ADAS; 601.042; 600.057 Approved no
Call Number Admin @ si @ Amo2013 Serial 2273
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Author Francesco Brughi
Title Artistic Heritage Motive Retrieval: an Explorative Study Type Report
Year 2013 Publication CVC Technical Report Abbreviated Journal
Volume (down) 176 Issue Pages
Keywords
Abstract
Address
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 IAM Approved no
Call Number Admin @ si @ Bru2013 Serial 2410
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Author Ivet Rafegas
Title Exploring Low-Level Vision Models. Case Study: Saliency Prediction Type Report
Year 2013 Publication CVC Technical Report Abbreviated Journal
Volume (down) 175 Issue Pages
Keywords
Abstract
Address
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 CIC Approved no
Call Number Admin @ si @ Raf2013 Serial 2409
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo
Title Mechanistic analysis of the function of agonists and allosteric modulators: Reconciling two-state and operational models Type Journal Article
Year 2013 Publication British Journal of Pharmacology Abbreviated Journal BJP
Volume (down) 169 Issue 6 Pages 1189-202
Keywords
Abstract Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism.
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 IAM; 600.044; 605.203 Approved no
Call Number IAM @ iam @ RGG2013b Serial 2195
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Author Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca
Title Large scale continuous visual event recognition using max-margin Hough transformation framework Type Journal Article
Year 2013 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume (down) 117 Issue 10 Pages 1356–1368
Keywords
Abstract In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions.
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 1077-3142 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ CGR2013 Serial 2413
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg
Title Coloring Action Recognition in Still Images Type Journal Article
Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume (down) 105 Issue 3 Pages 205-221
Keywords
Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes CIC; ADAS; 600.057; 600.048 Approved no
Call Number Admin @ si @ KRW2013 Serial 2285
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Author Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders
Title Selective Search for Object Recognition Type Journal Article
Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume (down) 104 Issue 2 Pages 154-171
Keywords
Abstract This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html).
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 0920-5691 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ USG2013 Serial 2362
Permanent link to this record
 
 
Author Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction Type Journal Article
Year 2013 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume (down) 100 Issue Pages 183–196
Keywords Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction
Abstract This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier 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 ISE Approved no
Call Number Admin @ si @ HAR2013 Serial 1808
Permanent link to this record