toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author Albert Gordo; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
openurl 
  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 (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-0-7695-4661-2 Medium  
  Area Expedition Conference DAS  
  Notes 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 edit   pdf
doi  isbn
openurl 
  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 (up) 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 DAG Approved no  
  Call Number Admin @ si @ GRP2012 Serial 2050  
Permanent link to this record
 

 
Author Francisco Cruz; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Document segmentation using relative location features Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1562-1565  
  Keywords  
  Abstract In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ CrR2012 Serial 2051  
Permanent link to this record
 

 
Author Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke edit   pdf
isbn  openurl
  Title Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 701-704  
  Keywords  
  Abstract Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FZE2012 Serial 2052  
Permanent link to this record
 

 
Author Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados edit   pdf
isbn  openurl
  Title Multipage Document Retrieval by Textual and Visual Representations Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 521-524  
  Keywords  
  Abstract In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RKB2012 Serial 2053  
Permanent link to this record
 

 
Author Marçal Rusiñol; Josep Llados edit  doi
isbn  openurl
  Title The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 55-60  
  Keywords  
  Abstract In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.  
  Address Bari, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RuL2012 Serial 2054  
Permanent link to this record
 

 
Author Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke edit   pdf
isbn  openurl
  Title Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 49-54  
  Keywords  
  Abstract State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.  
  Address Bari, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 10.1109/ICFHR.2012.268 ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FBF2012 Serial 2055  
Permanent link to this record
 

 
Author Emanuel Indermühle; Volkmar Frinken; Horst Bunke edit   pdf
doi  isbn
openurl 
  Title Mode Detection in Online Handwritten Documents using BLSTM Neural Networks Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 302-307  
  Keywords  
  Abstract Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.  
  Address Bari, italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ IFB2012 Serial 2056  
Permanent link to this record
 

 
Author Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier edit   pdf
doi  isbn
openurl 
  Title Bidirectional Language Model for Handwriting Recognition Type Conference Article
  Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal  
  Volume 7626 Issue Pages 611-619  
  Keywords  
  Abstract In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.  
  Address Japan  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) 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 DAG Approved no  
  Call Number Admin @ si @ FFL2012 Serial 2057  
Permanent link to this record
 

 
Author Laura Igual; Joan Carles Soliva; Roger Gimeno; Sergio Escalera; Oscar Vilarroya; Petia Radeva edit   pdf
doi  isbn
openurl 
  Title Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder Type Conference Article
  Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 7325 Issue II Pages 222-229  
  Keywords  
  Abstract Poster
Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.
 
  Address Aveiro, Portugal  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-31297-7 Medium  
  Area Expedition Conference ICIAR  
  Notes OR; HuPBA; MILAB Approved no  
  Call Number Admin @ si @ ISG2012 Serial 2059  
Permanent link to this record
 

 
Author Ekaterina Zaytseva; Jordi Vitria edit   pdf
doi  isbn
openurl 
  Title A search based approach to non maximum suppression in face detection Type Conference Article
  Year 2012 Publication 19th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Poster
paper TA.P5.12
Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results.
 
  Address Orlando; USA; September 2012  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1522-4880 ISBN 978-1-4673-2534-9 Medium  
  Area Expedition Conference ICIP  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ ZaV2012 Serial 2060  
Permanent link to this record
 

 
Author Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Moving object detection from mobile platforms using stereo data registration Type Book Chapter
  Year 2012 Publication Computational Intelligence paradigms in advanced pattern classification Abbreviated Journal  
  Volume 386 Issue Pages 25-37  
  Keywords pedestrian detection  
  Abstract This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor Marek R. Ogiela; Lakhmi C. Jain  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1860-949X ISBN 978-3-642-24048-5 Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ SGD2012 Serial 2061  
Permanent link to this record
 

 
Author Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez edit   pdf
doi  isbn
openurl 
  Title Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance Type Book Chapter
  Year 2012 Publication Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence Abbreviated Journal  
  Volume 384 Issue 3 Pages 87-95  
  Keywords  
  Abstract The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1860-949X ISBN 978-3-642-24033-1 Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ BFR2012 Serial 2062  
Permanent link to this record
 

 
Author Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus edit   pdf
openurl 
  Title Color Naming Type Book Chapter
  Year 2012 Publication Color in Computer Vision: Fundamentals and Applications Abbreviated Journal  
  Volume Issue 17 Pages 287-317  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher John Wiley & Sons, Ltd. Place of Publication Editor Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ WBV2012 Serial 2063  
Permanent link to this record
 

 
Author Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo edit   pdf
doi  openurl
  Title Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models Type Book Chapter
  Year 2012 Publication Vision Robotics: Technologies for Machine Learning and Vision Applications Abbreviated Journal  
  Volume Issue 2 Pages 23-42  
  Keywords  
  Abstract Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.  
  Address  
  Corporate Author Thesis  
  Publisher IGI-Global Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor (up) Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ PIE2012 Serial 2064  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: