| Records |
| 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 |
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| 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. |
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Edition |
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| ISSN |
0162-8828 |
ISBN |
978-1-4577-0394-2 |
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Notes  |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GLG 2012b |
Serial |
2008 |
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| Author |
Dimosthenis Karatzas;Ch. Lioutas |
| Title |
Software Package Development for Electron Diffraction Image Analysis |
Type |
Conference Article |
| Year |
1998 |
Publication |
Proceedings of the XIV Solid State Physics National Conference |
Abbreviated Journal |
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| Address |
Ioannina, Greece |
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DAG |
Approved |
no |
| Call Number |
IAM @ iam @ KaL1998 |
Serial |
2045 |
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| 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 |
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| Volume |
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Issue |
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Pages |
33-37 |
| Keywords |
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| 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 |
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Thesis |
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| Publisher |
IEEE Computer Society Washington |
Place of Publication |
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Editor |
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Edition |
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| ISSN |
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ISBN |
978-0-7695-4661-2 |
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Expedition |
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Conference |
DAS |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GPV2012 |
Serial |
2049 |
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| 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 |
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| Volume |
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Issue |
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Pages |
3045-3052 |
| Keywords |
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| 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 |
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Thesis |
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| Publisher |
IEEE Xplore |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GRP2012 |
Serial |
2050 |
| Permanent link to this record |
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| Author |
Francisco Cruz; Oriol Ramos Terrades |
| Title |
Document segmentation using relative location features |
Type |
Conference Article |
| 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 |
| Keywords |
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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. |
| Address |
Tsukuba Science City, Japan |
| Corporate Author |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ CrR2012 |
Serial |
2051 |
| Permanent link to this record |
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| Author |
Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke |
| 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 |
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| Volume |
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Issue |
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Pages |
701-704 |
| Keywords |
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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. |
| Address |
Tsukuba Science City, Japan |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FZE2012 |
Serial |
2052 |
| Permanent link to this record |
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| Author |
Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
| Title |
Multipage Document Retrieval by Textual and Visual Representations |
Type |
Conference Article |
| 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 |
521-524 |
| Keywords |
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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. |
| Address |
Tsukuba Science City, Japan |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ RKB2012 |
Serial |
2053 |
| Permanent link to this record |
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| Author |
Marçal Rusiñol; Josep Llados |
| 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 |
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| Volume |
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Issue |
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Pages |
55-60 |
| Keywords |
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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. |
| Address |
Bari, Italy |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4673-2262-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ RuL2012 |
Serial |
2054 |
| Permanent link to this record |
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| Author |
Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke |
| 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 |
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| Volume |
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Issue |
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Pages |
49-54 |
| Keywords |
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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. |
| Address |
Bari, Italy |
| Corporate Author |
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Thesis |
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Place of Publication |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
10.1109/ICFHR.2012.268 |
ISBN |
978-1-4673-2262-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FBF2012 |
Serial |
2055 |
| Permanent link to this record |
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| Author |
Emanuel Indermühle; Volkmar Frinken; Horst Bunke |
| 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 |
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| Volume |
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Issue |
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Pages |
302-307 |
| Keywords |
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| 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 |
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Thesis |
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Place of Publication |
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Editor |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4673-2262-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ IFB2012 |
Serial |
2056 |
| Permanent link to this record |
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| Author |
Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier |
| 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 |
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| Volume |
7626 |
Issue |
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Pages |
611-619 |
| Keywords |
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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. |
| Address |
Japan |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-34165-6 |
Medium |
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| Area |
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Expedition |
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Conference |
SSPR&SPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FFL2012 |
Serial |
2057 |
| Permanent link to this record |
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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 |
| Title |
CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012 |
Type |
Conference Article |
| Year |
2012 |
Publication |
Conference and Labs of the Evaluation Forum |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
Roma |
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Edition |
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ISBN |
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Expedition |
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Conference |
CLEF |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ RHM2012 |
Serial |
2072 |
| Permanent link to this record |
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| Author |
Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal |
| Title |
Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents |
Type |
Conference Article |
| 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 |
1663-1666 |
| Keywords |
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| Abstract |
This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging. |
| Address |
Tsukuba, Japan |
| Corporate Author |
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Place of Publication |
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Original Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ DGL2012 |
Serial |
2125 |
| Permanent link to this record |
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| Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
| Title |
Hierarchical graph representation for symbol spotting in graphical document images |
Type |
Conference Article |
| Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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| Volume |
7626 |
Issue |
|
Pages |
529-538 |
| Keywords |
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| Abstract |
Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
| Address |
Miyajima-Itsukushima, Hiroshima |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-34165-6 |
Medium |
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| Area |
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Expedition |
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Conference |
SSPR&SPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ BDJ2012 |
Serial |
2126 |
| Permanent link to this record |
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| Author |
Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta |
| Title |
On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents |
Type |
Journal Article |
| Year |
2012 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
Abbreviated Journal |
IJPRAI |
| Volume |
26 |
Issue |
5 |
Pages |
1263002-126027 |
| Keywords |
Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 |
| Abstract |
0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ LRF2012 |
Serial |
2128 |
| Permanent link to this record |
