|
Records |
Links |
|
Author |
Antonio Clavelli |
|
|
Title |
A computational model of eye guidance, searching for text in real scene images |
Type |
Book Whole |
|
Year |
2014 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Searching for text objects in real scene images is an open problem and a very active computer vision research area. A large number of methods have been proposed tackling the text search as extension of the ones from the document analysis field or inspired by general purpose object detection methods. However the general problem of object search in real scene images remains an extremely challenging problem due to the huge variability in object appearance. This thesis builds on top of the most recent findings in the visual attention literature presenting a novel computational model of eye guidance aiming to better describe text object search in real scene images.
First are presented the relevant state-of-the-art results from the visual attention literature regarding eye movements and visual search. Relevant models of attention are discussed and integrated with recent observations on the role of top-down constraints and the emerging need for a layered model of attention in which saliency is not the only factor guiding attention. Visual attention is then explained by the interaction of several modulating factors, such as objects, value, plans and saliency. Then we introduce our probabilistic formulation of attention deployment in real scene. The model is based on the rationale that oculomotor control depends on two interacting but distinct processes: an attentional process that assigns value to the sources of information and motor process that flexibly links information with action.
In such framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the reward of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects.
In the experimental section the model is tested in laboratory condition, comparing model simulations with data from eye tracking experiments. The comparison is qualitative in terms of observable scan paths and quantitative in terms of statistical similarity of gaze shift amplitude. Experiments are performed using eye tracking data from both a publicly available dataset of face and text and from newly performed eye-tracking experiments on a dataset of street view pictures containing text. The last part of this thesis is dedicated to study the extent to which the proposed model can account for human eye movements in a low constrained setting. We used a mobile eye tracking device and an ad-hoc developed methodology to compare model simulated eye data with the human eye data from mobile eye tracking recordings. Such setting allow to test the model in an incomplete visual information condition, reproducing a close to real-life search task. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
|
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Dimosthenis Karatzas;Giuseppe Boccignone;Josep Llados |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-84-940902-6-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Cla2014 |
Serial |
2571 |
|
Permanent link to this record |
|
|
|
|
Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
|
|
Title |
A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting |
Type |
Book Chapter |
|
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
|
|
|
Volume |
8746 |
Issue |
|
Pages |
7-11 |
|
|
Keywords |
Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel |
|
|
Abstract |
Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (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 edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge 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 the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
Bart Lamiroy; Jean-Marc Ogier |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DLB2014 |
Serial |
2698 |
|
Permanent link to this record |
|
|
|
|
Author |
Anjan Dutta |
|
|
Title |
Inexact Subgraph Matching Applied to Symbol Spotting in Graphical Documents |
Type |
Book Whole |
|
Year |
2014 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
There is a resurgence in the use of structural approaches in the usual object recognition and retrieval problem. Graph theory, in particular, graph matching plays a relevant role in that. Specifically, the detection of an object (or a part of that) in an image in terms of structural features can be formulated as a subgraph matching. Subgraph matching is a challenging task. Specially due to the presence of outliers most of the graph matching algorithms do not perform well in subgraph matching scenario. Also exact subgraph isomorphism has proven to be an NP-complete problem. So naturally, in graph matching community, there are lot of efforts addressing the problem of subgraph matching within suboptimal bound. Most of them work with approximate algorithms that try to get an inexact solution in estimated way. In addition, usual recognition must cope with distortion. Inexact graph matching consists in finding the best isomorphism under a similarity measure. Theoretically this thesis proposes algorithms for solving subgraph matching in an approximate and inexact way.
We consider the symbol spotting problem on graphical documents or line drawings from application point of view. This is a well known problem in the graphics recognition community. It can be further applied for indexing and classification of documents based on their contents. The structural nature of this kind of documents easily motivates one for giving a graph based representation. So the symbol spotting problem on graphical documents can be considered as a subgraph matching problem. The main challenges in this application domain is the noise and distortions that might come during the usage, digitalization and raster to vector conversion of those documents. Apart from that computer vision nowadays is not any more confined within a limited number of images. So dealing a huge number of images with graph based method is a further challenge.
In this thesis, on one hand, we have worked on efficient and robust graph representation to cope with the noise and distortions coming from documents. On the other hand, we have worked on different graph based methods and framework to solve the subgraph matching problem in a better approximated way, which can also deal with considerable number of images. Firstly, we propose a symbol spotting method by hashing serialized subgraphs. Graph serialization allows to create factorized substructures such as graph paths, which can be organized in hash tables depending on the structural similarities of the serialized subgraphs. The involvement of hashing techniques helps to reduce the search space substantially and speeds up the spotting procedure. Secondly, we introduce contextual similarities based on the walk based propagation on tensor product graph. These contextual similarities involve higher order information and more reliable than pairwise similarities. We use these higher order similarities to formulate subgraph matching as a node and edge selection problem in the tensor product graph. Thirdly, we propose near convex grouping to form near convex region adjacency graph which eliminates the limitations of traditional region adjacency graph representation for graphic recognition. Fourthly, we propose a hierarchical graph representation by simplifying/correcting the structural errors to create a hierarchical graph of the base graph. Later these hierarchical graph structures are matched with some graph matching methods. Apart from that, in this thesis we have provided an overall experimental comparison of all the methods and some of the state-of-the-art methods. Furthermore, some dataset models have also been proposed. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
|
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Josep Llados;Umapada Pal |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-84-940902-4-0 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Dut2014 |
Serial |
2465 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta |
|
|
Title |
The ICDAR/GREC 2013 Music Scores Competition: Staff Removal |
Type |
Book Chapter |
|
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
|
|
|
Volume |
8746 |
Issue |
|
Pages |
207-220 |
|
|
Keywords |
Competition; Graphics recognition; Music scores; Writer identification; Staff removal |
|
|
Abstract |
The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
B.Lamiroy; J.-M. Ogier |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077; 600.061 |
Approved |
no |
|
|
Call Number |
Admin @ si @ FKV2014 |
Serial |
2581 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre |
|
|
Title |
A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts |
Type |
Conference Article |
|
Year |
2014 |
Publication |
Digital Access to Textual Cultural Heritage Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
103-108 |
|
|
Keywords |
|
|
|
Abstract |
In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts. |
|
|
Address |
Madrid; May 2014 |
|
|
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 |
978-1-4503-2588-2 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
DATeCH |
|
|
Notes |
DAG; 600.061; 602.006; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ FLM2014 |
Serial |
2516 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Gemma Sanchez |
|
|
Title |
Analysis and Recognition of Music Scores |
Type |
Book Chapter |
|
Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
|
|
|
Volume |
E |
Issue |
|
Pages |
749-774 |
|
|
Keywords |
|
|
|
Abstract |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer London |
Place of Publication |
|
Editor |
D. Doermann; K. Tombre |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-0-85729-860-7 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
|
Permanent link to this record |
|
|
|
|
Author |
Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual |
|
|
Title |
Spatial Discriminant ICA for RS-fMRI characterisation |
Type |
Conference Article |
|
Year |
2014 |
Publication |
4th International Workshop on Pattern Recognition in Neuroimaging |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1-4 |
|
|
Keywords |
|
|
|
Abstract |
Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. |
|
|
Address |
Tübingen; June 2014 |
|
|
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 |
978-1-4799-4150-6 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
PRNI |
|
|
Notes |
OR;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ TBI2014 |
Serial |
2493 |
|
Permanent link to this record |
|
|
|
|
Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
|
|
Title |
Asymmetric Distances for Binary Embeddings |
Type |
Journal Article |
|
Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
36 |
Issue |
1 |
Pages |
33-47 |
|
|
Keywords |
|
|
|
Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
|
|
Address |
|
|
|
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 |
0162-8828 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.045; 605.203; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GPG2014 |
Serial |
2272 |
|
Permanent link to this record |
|
|
|
|
Author |
Agata Lapedriza; David Masip; David Sanchez |
|
|
Title |
Emotions Classification using Facial Action Units Recognition |
Type |
Conference Article |
|
Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
|
|
|
Volume |
269 |
Issue |
|
Pages |
55-64 |
|
|
Keywords |
|
|
|
Abstract |
In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
|
|
Address |
|
|
|
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 |
978-1-61499-451-0 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CCIA |
|
|
Notes |
OR;MV |
Approved |
no |
|
|
Call Number |
Admin @ si @ LMS2014 |
Serial |
2622 |
|
Permanent link to this record |
|
|
|
|
Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
|
|
Title |
No more meta-parameter tuning in unsupervised sparse feature learning |
Type |
Miscellaneous |
|
Year |
2014 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RRG2014 |
Serial |
2471 |
|
Permanent link to this record |
|
|
|
|
Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
|
|
Title |
Unsupervised Deep Feature Extraction Of Hyperspectral Images |
Type |
Conference Article |
|
Year |
2014 |
Publication |
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification |
|
|
Abstract |
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. |
|
|
Address |
Lausanne; Switzerland; June 2014 |
|
|
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 |
WHISPERS |
|
|
Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RGC2014 |
Serial |
2513 |
|
Permanent link to this record |
|
|
|
|
Author |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
|
|
Title |
Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization |
Type |
Conference Article |
|
Year |
2014 |
Publication |
25th British Machine Vision Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. |
|
|
Address |
Nottingham; UK; September 2014 |
|
|
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 |
BMVC |
|
|
Notes |
LAMP; CIC; 600.074; 600.079 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RWB2014 |
Serial |
2508 |
|
Permanent link to this record |
|
|
|
|
Author |
A.Kesidis; Dimosthenis Karatzas |
|
|
Title |
Logo and Trademark Recognition |
Type |
Book Chapter |
|
Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
|
|
|
Volume |
D |
Issue |
|
Pages |
591-646 |
|
|
Keywords |
Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
|
|
Abstract |
The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer London |
Place of Publication |
|
Editor |
D. Doermann; K. Tombre |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-0-85729-858-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ KeK2014 |
Serial |
2425 |
|
Permanent link to this record |