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Author |
Ekaterina Zaytseva; Santiago Segui; Jordi Vitria |
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Title |
Sketchable Histograms of Oriented Gradients for Object Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
17th Iberomerican Conference on Pattern Recognition |
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Volume |
7441 |
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Pages |
374-381 |
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Abstract |
In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature. |
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Buenos Aires, Argentina |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33274-6 |
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CIARP |
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Notes |
OR; MILAB;MV |
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no |
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Call Number |
Admin @ si @ ZSV2012 |
Serial |
2048 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Document classification using multiple views |
Type |
Conference Article |
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Year |
2012 |
Publication |
10th IAPR International Workshop on Document Analysis Systems |
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Pages |
33-37 |
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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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Australia |
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IEEE Computer Society Washington |
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978-0-7695-4661-2 |
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DAS |
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DAG |
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no |
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Call Number |
Admin @ si @ GPV2012 |
Serial |
2049 |
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Author |
Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny |
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Title |
Leveraging category-level labels for instance-level image retrieval |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
3045-3052 |
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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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Providence, Rhode Island |
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IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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Conference |
CVPR |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ GRP2012 |
Serial |
2050 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Document segmentation using relative location features |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1562-1565 |
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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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Tsukuba Science City, Japan |
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ICPR |
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DAG |
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no |
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Call Number |
Admin @ si @ CrR2012 |
Serial |
2051 |
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Author |
Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke |
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Title |
Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
701-704 |
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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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Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ FZE2012 |
Serial |
2052 |
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Permanent link to this record |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
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Title |
Multipage Document Retrieval by Textual and Visual Representations |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Pages |
521-524 |
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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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Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ RKB2012 |
Serial |
2053 |
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Permanent link to this record |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback |
Type |
Conference Article |
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Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
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Volume |
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Pages |
55-60 |
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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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Address |
Bari, Italy |
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ISBN |
978-1-4673-2262-1 |
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Conference |
ICFHR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ RuL2012 |
Serial |
2054 |
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Permanent link to this record |
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Author |
Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke |
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Title |
Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting |
Type |
Conference Article |
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Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
49-54 |
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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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Bari, Italy |
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ISSN |
10.1109/ICFHR.2012.268 |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ FBF2012 |
Serial |
2055 |
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Author |
Emanuel Indermühle; Volkmar Frinken; Horst Bunke |
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Title |
Mode Detection in Online Handwritten Documents using BLSTM Neural Networks |
Type |
Conference Article |
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Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Volume |
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Pages |
302-307 |
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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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Address |
Bari, italy |
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978-1-4673-2262-1 |
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ICFHR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ IFB2012 |
Serial |
2056 |
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Permanent link to this record |
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Author |
Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier |
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Title |
Bidirectional Language Model for Handwriting Recognition |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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Volume |
7626 |
Issue |
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Pages |
611-619 |
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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. |
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Address |
Japan |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-34165-6 |
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SSPR&SPR |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ FFL2012 |
Serial |
2057 |
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Author |
Laura Igual; Joan Carles Soliva; Roger Gimeno; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title |
Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder |
Type |
Conference Article |
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Year |
2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
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Volume |
7325 |
Issue |
II |
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222-229 |
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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. |
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Aveiro, Portugal |
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0302-9743 |
ISBN |
978-3-642-31297-7 |
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ICIAR |
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Notes |
OR; HuPBA; MILAB |
Approved |
no |
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Call Number |
Admin @ si @ ISG2012 |
Serial |
2059 |
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Permanent link to this record |
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Author |
Ekaterina Zaytseva; Jordi Vitria |
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Title |
A search based approach to non maximum suppression in face detection |
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Conference Article |
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Year |
2012 |
Publication |
19th IEEE International Conference on Image Processing |
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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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Orlando; USA; September 2012 |
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1522-4880 |
ISBN |
978-1-4673-2534-9 |
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ICIP |
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Notes |
OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ ZaV2012 |
Serial |
2060 |
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Author |
Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera |
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Title |
Análisis Comportamental Automatizado de TDAH: la Influencia de la Variable Motivación |
Type |
Conference Article |
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Year |
2012 |
Publication |
IPSI – Cosmocaixa, Jornadas "Empremtes del present, efectes en la psicoanàlisi, la cultura i la societat |
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Poster |
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IPSI |
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MILAB; HuPBA; OR |
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no |
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Admin @ si @ EMI2012b |
Serial |
2065 |
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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Joan Mas; Oriol Ramos Terrades; Dimosthenis Karatzas; Anjan Dutta; Gemma Sanchez; Josep Llados |
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Title |
CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012 |
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Conference Article |
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2012 |
Publication |
Conference and Labs of the Evaluation Forum |
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Admin @ si @ RHM2012 |
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2072 |
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Author |
Miguel Angel Bautista; Antonio Hernandez; Victor Ponce; Xavier Perez Sala; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data |
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Conference Article |
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2012 |
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21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
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7854 |
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126-135 |
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Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40302-6 |
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WDIA |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ BHP2012 |
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2120 |
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