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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Three-Dimensional Design of Error Correcting Output Codes. In 8th International Conference on Machine Learning and Data Mining (pp. 29–).
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Error Correcting Output Codes for multiclass classification: Application to two image vision problems. In 16th symposium on Artificial Intelligence & Signal Processing (pp. 508–513). IEEE Xplore.
Abstract: Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches.
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Dimosthenis Karatzas, & Ch. Lioutas. (1998). Software Package Development for Electron Diffraction Image Analysis. In Proceedings of the XIV Solid State Physics National Conference.
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Albert Gordo, Florent Perronnin, & Ernest Valveny. (2012). Document classification using multiple views. In 10th IAPR International Workshop on Document Analysis Systems (pp. 33–37). IEEE Computer Society Washington.
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.
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Albert Gordo, Jose Antonio Rodriguez, Florent Perronnin, & Ernest Valveny. (2012). Leveraging category-level labels for instance-level image retrieval. In 25th IEEE Conference on Computer Vision and Pattern Recognition (pp. 3045–3052). IEEE Xplore.
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.
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Francisco Cruz, & Oriol Ramos Terrades. (2012). Document segmentation using relative location features. In 21st International Conference on Pattern Recognition (pp. 1562–1565).
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.
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Volkmar Frinken, Francisco Zamora, Salvador España, Maria Jose Castro, Andreas Fischer, & Horst Bunke. (2012). Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition. In 21st International Conference on Pattern Recognition (pp. 701–704).
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.
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Marçal Rusiñol, Dimosthenis Karatzas, Andrew Bagdanov, & Josep Llados. (2012). Multipage Document Retrieval by Textual and Visual Representations. In 21st International Conference on Pattern Recognition (pp. 521–524).
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.
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Marçal Rusiñol, & Josep Llados. (2012). The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 55–60).
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.
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Volkmar Frinken, Markus Baumgartner, Andreas Fischer, & Horst Bunke. (2012). Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 49–54).
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.
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Emanuel Indermühle, Volkmar Frinken, & Horst Bunke. (2012). Mode Detection in Online Handwritten Documents using BLSTM Neural Networks. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 302–307).
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.
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Ekaterina Zaytseva, & Jordi Vitria. (2012). A search based approach to non maximum suppression in face detection. In 19th IEEE International Conference on Image Processing.
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.
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Joost Van de Weijer, Robert Benavente, Maria Vanrell, Cordelia Schmid, Ramon Baldrich, Jacob Verbeek, et al. (2012). Color Naming. In Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & Jan-Mark Geusebroek (Eds.), Color in Computer Vision: Fundamentals and Applications (pp. 287–317). John Wiley & Sons, Ltd.
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Xavier Perez Sala, Laura Igual, Sergio Escalera, & Cecilio Angulo. (2012). Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models. In Vision Robotics: Technologies for Machine Learning and Vision Applications (pp. 23–42). IGI-Global.
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.
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Sergio Escalera, Josep Moya, Laura Igual, Veronica Violant, & Maria Teresa Anguera. (2012). Análisis Comportamental Automatizado de TDAH: la Influencia de la Variable Motivación. In IPSI – Cosmocaixa, Jornadas "Empremtes del present, efectes en la psicoanàlisi, la cultura i la societat.
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