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| Author | Joost Van de Weijer; Fahad Shahbaz Khan; Marc Masana | ||||
| Title | Interactive Visual and Semantic Image Retrieval | Type | Book Chapter | ||
| Year | 2013 | Publication  |
Multimodal Interaction in Image and Video Applications | Abbreviated Journal | |
| Volume | 48 | Issue | Pages | 31-35 | |
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| Abstract | One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Angel Sappa; Jordi Vitria | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | CIC; 605.203; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ WKC2013 | Serial | 2284 | ||
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| Author | Abel Gonzalez-Garcia; Robert Benavente; Olivier Penacchio; Javier Vazquez; Maria Vanrell; C. Alejandro Parraga | ||||
| Title | Coloresia: An Interactive Colour Perception Device for the Visually Impaired | Type | Book Chapter | ||
| Year | 2013 | Publication |
Multimodal Interaction in Image and Video Applications | Abbreviated Journal | |
| Volume | 48 | Issue | Pages | 47-66 | |
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| Abstract | A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves. | ||||
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| 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 | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | CIC; 600.052; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ GBP2013 | Serial | 2266 | ||
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| Author | Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
| Title | An Application for Efficient Error-Free Labeling of Medical Images | Type | Book Chapter | ||
| Year | 2013 | Publication |
Multimodal Interaction in Image and Video Applications | Abbreviated Journal | |
| Volume | 48 | Issue | Pages | 1-16 | |
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| Abstract | In this chapter we describe an application for efficient error-free labeling of medical images. In this scenario, the compilation of a complete training set for building a realistic model of a given class of samples is not an easy task, making the process tedious and time consuming. For this reason, there is a need for interactive labeling applications that minimize the effort of the user while providing error-free labeling. We propose a new algorithm that is based on data similarity in feature space. This method actively explores data in order to find the best label-aligned clustering and exploits it to reduce the labeler effort, that is measured by the number of “clicks. Moreover, error-free labeling is guaranteed by the fact that all data and their labels proposals are visually revised by en expert. | ||||
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| 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 | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ DSR2013 | Serial | 2235 | ||
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| Author | Angel Sappa; Jordi Vitria | ||||
| Title | Multimodal Interaction in Image and Video Applications | Type | Book Whole | ||
| Year | 2013 | Publication |
Multimodal Interaction in Image and Video Applications | Abbreviated Journal | |
| Volume | 48 | Issue | Pages | ||
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| Abstract | Book Series Intelligent Systems Reference Library | ||||
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| 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 | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ADAS; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SaV2013 | Serial | 2199 | ||
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| Author | Marc Castello; Jordi Gonzalez; Ariel Amato; Pau Baiget; Carles Fernandez; Josep M. Gonfaus; Ramon Mollineda; Marco Pedersoli; Nicolas Perez de la Blanca; Xavier Roca | ||||
| Title | Exploiting Multimodal Interaction Techniques for Video-Surveillance | Type | Book Chapter | ||
| Year | 2013 | Publication |
Multimodal Interaction in Image and Video Applications Intelligent Systems Reference Library | Abbreviated Journal | |
| Volume | 48 | Issue | 8 | Pages | 135-151 |
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| Abstract | In this paper we present an example of a video surveillance application that exploits Multimodal Interactive (MI) technologies. The main objective of the so-called VID-Hum prototype was to develop a cognitive artificial system for both the detection and description of a particular set of human behaviours arising from real-world events. The main procedure of the prototype described in this chapter entails: (i) adaptation, since the system adapts itself to the most common behaviours (qualitative data) inferred from tracking (quantitative data) thus being able to recognize abnormal behaviors; (ii) feedback, since an advanced interface based on Natural Language understanding allows end-users the communicationwith the prototype by means of conceptual sentences; and (iii) multimodality, since a virtual avatar has been designed to describe what is happening in the scene, based on those textual interpretations generated by the prototype. Thus, the MI methodology has provided an adequate framework for all these cooperating processes. | ||||
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| 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 | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE; 605.203; 600.049 | Approved | no | ||
| Call Number | CGA2013 | Serial | 2222 | ||
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| Author | David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo | ||||
| Title | Interactive Training of Human Detectors | Type | Book Chapter | ||
| Year | 2013 | Publication |
Multiodal Interaction in Image and Video Applications | Abbreviated Journal | |
| Volume | 48 | Issue | Pages | 169-182 | |
| Keywords | Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation | ||||
| Abstract | Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. | ||||
| Address | Springer Heidelberg New York Dordrecht London | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.057; 600.054; 605.203 | Approved | no | ||
| Call Number | VLP2013; ADAS @ adas @ vlp2013 | Serial | 2193 | ||
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| Author | Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters | ||||
| Title | Fractional Programming Weighted Decoding for Error-Correcting Output Codes | Type | Conference Article | ||
| Year | 2015 | Publication |
Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 38-50 | ||
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| Abstract | In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments. | ||||
| Address | Gunzburg; Germany; June 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-20247-1 | Medium | ||
| Area | Expedition | Conference | MCS | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ ISS2015 | Serial | 2601 | ||
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| Author | Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir | ||||
| Title | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge | Type | Conference Article | ||
| Year | 2020 | Publication |
MYOPS challenge and workshop | Abbreviated Journal | |
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| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ MAG2020 | Serial | 3518 | ||
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| Author | I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi | ||||
| Title | Learning quantification from images: A structured neural architecture | Type | Journal Article | ||
| Year | 2018 | Publication |
Natural Language Engineering | Abbreviated Journal | NLE |
| Volume | 24 | Issue | 3 | Pages | 363-392 |
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| Abstract | Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area. | ||||
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| Area | Expedition | Conference | |||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SPH2018 | Serial | 3021 | ||
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| Author | Matthias S. Keil | ||||
| Title | Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk | Type | Journal | ||
| Year | 2006 | Publication |
Neural Computation | Abbreviated Journal | NEURALCOMPUT |
| Volume | 18 | Issue | 4 | Pages | 871–903 |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Kei2006 | Serial | 633 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | A neurodynamic model of saliency prediction in v1 | Type | Journal Article | ||
| Year | 2022 | Publication |
Neural Computation | Abbreviated Journal | NEURALCOMPUT |
| Volume | 34 | Issue | 2 | Pages | 378-414 |
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| Abstract | Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex. | ||||
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| Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BeO2022 | Serial | 3696 | ||
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| Author | Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza | ||||
| Title | Evolving weighting schemes for the Bag of Visual Words | Type | Journal Article | ||
| Year | 2017 | Publication |
Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 925–939 |
| Keywords | Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision | ||||
| Abstract | The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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| Publisher | Place of Publication | Editor | Springer | ||
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| Notes | HUPBA;MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPE2017 | Serial | 2743 | ||
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| Author | Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla | ||||
| Title | Editorial: Special Issue on Computational Intelligence for Vision and Robotics | Type | Journal Article | ||
| Year | 2017 | Publication |
Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 853–854 |
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| Notes | HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ GGE2017 | Serial | 2845 | ||
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| Author | Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition | Type | Journal Article | ||
| Year | 2020 | Publication |
Neural Computing and Applications | Abbreviated Journal | NEUCOMA |
| Volume | 32 | Issue | Pages | 11579–11596 | |
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| Abstract | Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods. | ||||
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| Notes | DAG; 600.140; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ DRL2020 | Serial | 3348 | ||
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| Author | Matthias S. Keil; Gabriel Cristobal; Thorsten Hansen; Heiko Neumann | ||||
| Title | Recovering real-world images from single-scale boundaries with a novel filling-in architecture | Type | Journal | ||
| Year | 2005 | Publication |
Neural Networks 18(10):1319–1331 (IF: 1.665) | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ KCH2005 | Serial | 576 | ||
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