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Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
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Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
Type |
Journal Article |
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Year |
2013 |
Publication |
Expert Systems |
Abbreviated Journal |
EXSY |
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Volume |
30 |
Issue |
2 |
Pages |
101-114 |
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Keywords |
Human action recognition;body-part detection;hidden Markov model |
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Abstract |
This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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ISE |
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no |
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Admin @ si @ CBG2013 |
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1809 |
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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Title |
Quantitative analysis of non-verbal communication for competence analysis |
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Conference Article |
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Year |
2013 |
Publication |
16th Catalan Conference on Artificial Intelligence |
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Volume |
256 |
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105-114 |
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Vic; October 2013 |
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CCIA |
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HUPBA;MILAB |
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no |
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Admin @ si @ CCE2013 |
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2324 |
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Author |
Nuria Cirera; Alicia Fornes; Volkmar Frinken; Josep Llados |
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Title |
Hybrid grammar language model for handwritten historical documents recognition |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
7887 |
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Pages |
117-124 |
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In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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Notes |
DAG; 602.006; 600.045; 600.061 |
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no |
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Call Number |
Admin @ si @ CFF2013 |
Serial |
2292 |
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Author |
Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca |
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Title |
Large scale continuous visual event recognition using max-margin Hough transformation framework |
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Journal Article |
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Year |
2013 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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Volume |
117 |
Issue |
10 |
Pages |
1356–1368 |
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In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions. |
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1077-3142 |
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ISE |
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no |
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Call Number |
Admin @ si @ CGR2013 |
Serial |
2413 |
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Author |
Francesco Ciompi; Rui Hua; Simone Balocco; Marina Alberti; Oriol Pujol; Carles Caus; J. Mauri; Petia Radeva |
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Title |
Learning to Detect Stent Struts in Intravascular Ultrasound |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
7887 |
Issue |
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Pages |
575-583 |
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Abstract |
In this paper we tackle the automatic detection of struts elements (metallic braces of a stent device) in Intravascular Ultrasound (IVUS) sequences. The proposed method is based on context-aware classification of IVUS images, where we use Multi-Class Multi-Scale Stacked Sequential Learning (M2SSL). Additionally, we introduce a novel technique to reduce the amount of required contextual features. The comparison with binary and multi-class learning is also performed, using a dataset of IVUS images with struts manually annotated by an expert. The best performing configuration reaches a F-measure F = 63.97% . |
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Address |
Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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Notes |
MILAB; HuPBA; 605.203; 600.046 |
Approved |
no |
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Call Number |
Admin @ si @ CHB2013 |
Serial |
2349 |
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Permanent link to this record |
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Author |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Towards Modelling an Attention-Based Text Localization Process |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
7887 |
Issue |
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Pages |
296-303 |
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Keywords |
text localization; visual attention; eye guidance |
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Abstract |
This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
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Conference |
IbPRIA |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ CKL2013 |
Serial |
2291 |
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Permanent link to this record |
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Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
Multi-modal User Identification and Object Recognition Surveillance System |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
34 |
Issue |
7 |
Pages |
799-808 |
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Keywords |
Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
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Abstract |
We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Elsevier |
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Notes |
HUPBA; 600.046; 605.203;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ CRE2013 |
Serial |
2248 |
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Permanent link to this record |
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Author |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
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Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
Type |
Journal Article |
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Year |
2013 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
EXWA |
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Volume |
40 |
Issue |
17 |
Pages |
6707-6712 |
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Keywords |
Neural gas; Expert vision; Eye-tracking; Fixations |
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Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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0957-4174 |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ CRM2013 |
Serial |
2438 |
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Permanent link to this record |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Handwritten Line Detection via an EM Algorithm |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Pages |
718-722 |
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In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ CrT2013 |
Serial |
2329 |
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Author |
Fadi Dornaika; Alireza Bosaghzadeh; Bogdan Raducanu |
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Title |
Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition |
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Conference Article |
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Year |
2013 |
Publication |
Human Behavior Understanding 4th International Workshop |
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Volume |
8212 |
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124-135 |
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Abstract |
Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods. |
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Barcelona |
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Springer International Publishing |
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0302-9743 |
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978-3-319-02713-5 |
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HBU |
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OR;MV |
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no |
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Admin @ si @ DBR2013 |
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2315 |
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Author |
Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
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Title |
Analysis and Retrieval of Tracked Events and Motion in Imagery Streams |
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Miscellaneous |
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2013 |
Publication |
ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream |
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Barcelona; October 2013 |
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ISE |
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no |
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Admin @ si @ DDB2013 |
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2372 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
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Conference Article |
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Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
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8228 |
Issue |
III |
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368-375 |
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An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Address |
Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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no |
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Call Number |
Admin @ si @ DFD2013 |
Serial |
2439 |
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Permanent link to this record |
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Author |
Katerine Diaz; Francesc J. Ferri |
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Title |
Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes |
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Book Whole |
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Year |
2013 |
Publication |
Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes |
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Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes. |
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978-3-639-55339-0 |
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ADAS |
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no |
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Call Number |
Admin @ si @ DiF2013 |
Serial |
2440 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1078-1082 |
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This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.045; 600.056; 600.061; 601.152 |
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Admin @ si @ DLB2013a |
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2358 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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A Product graph based method for dual subgraph matching applied to symbol spotting |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Product graph has been shown to be an efficient way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. This paper focuses on the two major limitations of the previous version of product graph: (1) Spurious nodes and edges in the graph representation and (2) Inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual graph representation on the original graph representing the graphical information and the product graph is computed between the dual graphs of the query graphs and the input graph.
The dual graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates similar path information of two graphs and exponentiating the adjacency matrix finds similar paths of greater lengths. Nodes joining similar paths between two graphs are found by combining different exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG |
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Admin @ si @ DLB2013b |
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2359 |
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