| Records |
| Author |
Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel |
| Title |
An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres |
Type |
Book Chapter |
| Year |
2015 |
Publication |
The White Book on Public Library Network from Diputació de Barcelona |
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| Notes |
MV; DAG;SIAI |
Approved |
no |
| Call Number |
Admin @ si @VKC2015 |
Serial |
2798 |
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| Author |
Fernando Vilariño |
| Title |
Computer Vision and Performing Arts |
Type |
Conference Article |
| Year |
2015 |
Publication |
Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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Conference |
KAMS |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @Vil2015 |
Serial |
2799 |
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| Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
| Title |
Memory Fields: DJs in the Library |
Type |
Conference Article |
| Year |
2015 |
Publication |
21 st Symposium of Electronic Arts |
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Vancouver; Canada; August 2015 |
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Conference |
ISEA |
| Notes |
;SIAI |
Approved |
no |
| Call Number |
Admin @ si @VNF2015 |
Serial |
2800 |
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| Author |
Pau Riba; Alicia Fornes; Josep Llados |
| Title |
Towards the Alignment of Handwritten Music Scores |
Type |
Conference Article |
| Year |
2015 |
Publication |
11th IAPR International Workshop on Graphics Recognition |
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It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
| Address |
Nancy; France; August 2015 |
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| Publisher |
Springer International Publishing |
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Editor |
Bart Lamiroy; Rafael Dueire Lins |
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LNCS |
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ISBN |
978-3-319-52158-9 |
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Conference |
GREC |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
2874 |
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| Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
| Title |
Automatic Verification of Properly Signed Multi-page Document Images |
Type |
Conference Article |
| Year |
2015 |
Publication |
Proceedings of the Eleventh International Symposium on Visual Computing |
Abbreviated Journal |
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| Volume |
9475 |
Issue |
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Pages |
327-336 |
| Keywords |
Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
| Abstract |
In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
| Address |
Las Vegas, Nevada, USA; December 2015 |
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LNCS |
| Series Volume |
9475 |
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Conference |
ISVC |
| Notes |
DAG; 600.077 |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3189 |
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| Author |
G. Lisanti; I. Masi; Andrew Bagdanov; Alberto del Bimbo |
| Title |
Person Re-identification by Iterative Re-weighted Sparse Ranking |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
37 |
Issue |
8 |
Pages |
1629 - 1642 |
| Keywords |
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| Abstract |
In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second. |
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| ISSN |
0162-8828 |
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| Notes |
LAMP; 601.240; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ LMB2015 |
Serial |
2557 |
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| Author |
David Sanchez-Mendoza; David Masip; Agata Lapedriza |
| Title |
Emotion recognition from mid-level features |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
67 |
Issue |
Part 1 |
Pages |
66–74 |
| Keywords |
Facial expression; Emotion recognition; Action units; Computer vision |
| Abstract |
In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. |
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Elsevier B.V. |
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0167-8655 |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ SML2015 |
Serial |
2746 |
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| Author |
Jaume Amores |
| Title |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
| Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
| Volume |
42 |
Issue |
2 |
Pages |
381-407 |
| Keywords |
Multi-instance learning; Codebook; Bag of words |
| Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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Springer London |
Place of Publication |
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Edition |
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| ISSN |
0219-1377 |
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Expedition |
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Conference |
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| Notes |
ADAS; 601.042; 600.057; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ Amo2015 |
Serial |
2383 |
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| Author |
Marc Bolaños; Maite Garolera; Petia Radeva |
| Title |
Object Discovery using CNN Features in Egocentric Videos |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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| Volume |
9117 |
Issue |
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Pages |
67-74 |
| Keywords |
Object discovery; Egocentric videos; Lifelogging; CNN |
| Abstract |
Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach. |
| Address |
Santiago de Compostela; España; June 2015 |
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LNCS |
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Edition |
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0302-9743 |
ISBN |
978-3-319-19389-2 |
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Conference |
IbPRIA |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGR2015 |
Serial |
2596 |
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| Author |
Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
| Title |
R-clustering for egocentric video segmentation |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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| Volume |
9117 |
Issue |
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Pages |
327-336 |
| Keywords |
Temporal video segmentation; Egocentric videos; Clustering |
| Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
| Address |
Santiago de Compostela; España; June 2015 |
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Thesis |
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| Publisher |
Springer International Publishing |
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 |
978-3-319-19389-2 |
Medium |
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Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ TDB2015 |
Serial |
2597 |
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| Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
| Title |
Large-scale Graph Indexing using Binary Embeddings of Node Contexts |
Type |
Conference Article |
| Year |
2015 |
Publication |
10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition |
Abbreviated Journal |
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| Volume |
9069 |
Issue |
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Pages |
208-217 |
| Keywords |
Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding |
| Abstract |
Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents. |
| Address |
Beijing; China; May 2015 |
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Thesis |
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| Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng |
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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 |
978-3-319-18223-0 |
Medium |
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Expedition |
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Conference |
GbRPR |
| Notes |
DAG; 600.061; 602.006; 600.077 |
Approved |
no |
| Call Number |
Admin @ si @ RLF2015a |
Serial |
2618 |
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| Author |
Onur Ferhat; Arcadi Llanza; Fernando Vilariño |
| Title |
A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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| Volume |
9117 |
Issue |
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Pages |
569-576 |
| Keywords |
Eye tracking; Gaze estimation; Natural light; Webcam |
| Abstract |
We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system. |
| Address |
Santiago de Compostela; June 2015 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer International Publishing |
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 |
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Series Issue |
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Edition |
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0302-9743 |
ISBN |
978-3-319-19389-2 |
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Conference |
IbPRIA |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ FLV2015a |
Serial |
2646 |
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| Author |
Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos |
| Title |
A predictive model for human activity recognition by observing actions and context |
Type |
Conference Article |
| Year |
2015 |
Publication |
Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 |
Abbreviated Journal |
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| Volume |
9386 |
Issue |
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Pages |
323-333 |
| Keywords |
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| Abstract |
This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
| Address |
Catania; Italy; October 2015 |
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Thesis |
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| Publisher |
Springer International Publishing |
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 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 |
978-3-319-25902-4 |
Medium |
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Expedition |
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Conference |
ACIVS |
| Notes |
ADAS; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ RFS2015 |
Serial |
2661 |
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| Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
| Title |
Deep semantic pyramids for human attributes and action recognition |
Type |
Conference Article |
| Year |
2015 |
Publication |
Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
Abbreviated Journal |
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| Volume |
9127 |
Issue |
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Pages |
341-353 |
| Keywords |
Action recognition; Human attributes; Semantic pyramids |
| Abstract |
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
| Address |
Denmark; Copenhagen; June 2015 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer International Publishing |
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 Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-319-19664-0 |
Medium |
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Expedition |
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Conference |
SCIA |
| Notes |
LAMP; 600.068; 600.079;ADAS |
Approved |
no |
| Call Number |
Admin @ si @ KRW2015b |
Serial |
2672 |
| Permanent link to this record |
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| Author |
Suman Ghosh; Ernest Valveny |
| Title |
A Sliding Window Framework for Word Spotting Based on Word Attributes |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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| Volume |
9117 |
Issue |
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Pages |
652-661 |
| Keywords |
Word spotting; Sliding window; Word attributes |
| Abstract |
In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets. |
| Address |
Santiago de Compostela; June 2015 |
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| Publisher |
Springer International Publishing |
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LNCS |
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| ISSN |
0302-9743 |
ISBN |
978-3-319-19389-2 |
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Conference |
IbPRIA |
| Notes |
DAG; 600.077 |
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no |
| Call Number |
Admin @ si @ GhV2015b |
Serial |
2716 |
| Permanent link to this record |
