|
Records |
Links |
|
Author |
Ferran Diego; G.D. Evangelidis; Joan Serrat |
|
|
Title |
Night-time outdoor surveillance by mobile cameras |
Type |
Conference Article |
|
Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
|
|
|
Volume |
2 |
Issue |
|
Pages |
365-371 |
|
|
Keywords |
|
|
|
Abstract |
This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. |
|
|
Address |
Algarve, Portugal |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPRAM |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ DES2012 |
Serial |
2035 |
|
Permanent link to this record |
|
|
|
|
Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
|
|
Title |
Synthetic ground truth dataset to detect shadow cast by static objects in outdoor |
Type |
Conference Article |
|
Year |
2012 |
Publication |
1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
art. 11 |
|
|
Keywords |
|
|
|
Abstract |
In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software. |
|
|
Address |
Capri, Italy |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
ACM |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4503-1405-3 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
VIGTA |
|
|
Notes |
OR;MV |
Approved |
no |
|
|
Call Number |
Admin @ si @ ISR2012a |
Serial |
2037 |
|
Permanent link to this record |
|
|
|
|
Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
|
|
Title |
Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
Type |
Conference Article |
|
Year |
2012 |
Publication |
High Performance Computing and Simulation, International Conference on |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
182-187 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation. |
|
|
Address |
Madrid |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE Xplore |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4673-2359-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
HPCS |
|
|
Notes |
MILAB;HuPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ ISH2012a |
Serial |
2038 |
|
Permanent link to this record |
|
|
|
|
Author |
Sergio Escalera |
|
|
Title |
Human Behavior Analysis From Depth Maps |
Type |
Conference Article |
|
Year |
2012 |
Publication |
7th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
|
|
|
Volume |
7378 |
Issue |
|
Pages |
282-292 |
|
|
Keywords |
|
|
|
Abstract |
Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. |
|
|
Address |
Mallorca |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Heidelberg |
Place of Publication |
|
Editor |
F.J. Perales; R.B. Fisher; T.B. Moeslund |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-642-31566-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
AMDO |
|
|
Notes |
MILAB; HuPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ Esc2012 |
Serial |
2040 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
|
|
Title |
Three-Dimensional Design of Error Correcting Output Codes |
Type |
Conference Article |
|
Year |
2012 |
Publication |
8th International Conference on Machine Learning and Data Mining |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
29- |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Berlin, Germany |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
MLDM |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGE2012a |
Serial |
2041 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
|
|
Title |
Error Correcting Output Codes for multiclass classification: Application to two image vision problems |
Type |
Conference Article |
|
Year |
2012 |
Publication |
16th symposium on Artificial Intelligence & Signal Processing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
508-513 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
Shiraz, Iran |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE Xplore |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4673-1478-7 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
AISP |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGE2012b |
Serial |
2042 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
|
|
Title |
Efficient pairwise classification using Local Cross Off strategy |
Type |
Conference Article |
|
Year |
2012 |
Publication |
25th Canadian Conference on Artificial Intelligence |
Abbreviated Journal |
|
|
|
Volume |
7310 |
Issue |
|
Pages |
25-36 |
|
|
Keywords |
|
|
|
Abstract |
The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. |
|
|
Address |
Toronto, Ontario |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-642-30352-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
AI |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGE2012c |
Serial |
2044 |
|
Permanent link to this record |
|
|
|
|
Author |
Ekaterina Zaytseva; Santiago Segui; Jordi Vitria |
|
|
Title |
Sketchable Histograms of Oriented Gradients for Object Detection |
Type |
Conference Article |
|
Year |
2012 |
Publication |
17th Iberomerican Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
7441 |
Issue |
|
Pages |
374-381 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
Buenos Aires, Argentina |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-642-33274-6 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CIARP |
|
|
Notes |
OR; MILAB;MV |
Approved |
no |
|
|
Call Number |
Admin @ si @ ZSV2012 |
Serial |
2048 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
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 |
|
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 |
|
|
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 |
|
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 |
|
|
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 |
|
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 |
|
|
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 |
|
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 |
|
|
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 |
|
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 |
|
|
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 |
|
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 |
|
|
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 |
|
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 |