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Author |
Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera |
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Title |
Error-Correcting Factorization |
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Journal Article |
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2018 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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40 |
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2388-2401 |
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Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. |
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0162-8828 |
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HuPBA; no menciona |
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no |
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Admin @ si @ BPT2018 |
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3015 |
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I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi |
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Title |
Learning quantification from images: A structured neural architecture |
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Journal Article |
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2018 |
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Natural Language Engineering |
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NLE |
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24 |
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3 |
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363-392 |
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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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MILAB; no menciona |
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Admin @ si @ SPH2018 |
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3021 |
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Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri |
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Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction |
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Journal Article |
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2018 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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60 |
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4 |
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512-524 |
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This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 |
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no |
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Admin @ si @ DMH2018a |
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3062 |
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Author |
Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
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SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
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Miscellaneous |
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2018 |
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Arxiv |
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Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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DAG; 600.097; 600.121 |
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no |
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Call Number |
Admin @ si @ DDT2018 |
Serial |
3085 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
Type |
Conference Article |
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Year |
2018 |
Publication |
International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
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Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 600.121; |
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Admin @ si @ BKB2018b |
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3174 |
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Author |
Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu |
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Title |
Which tone-mapping operator is the best? A comparative study of perceptual quality |
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Journal Article |
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Year |
2018 |
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Journal of the Optical Society of America A |
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JOSA A |
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35 |
Issue |
4 |
Pages |
626-638 |
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Tone-mapping operators (TMO) are designed to generate perceptually similar low-dynamic range images from high-dynamic range ones. We studied the performance of fifteen TMOs in two psychophysical experiments where observers compared the digitally-generated tone-mapped images to their corresponding physical scenes. All experiments were performed in a controlled environment and the setups were
designed to emphasize different image properties: in the first experiment we evaluated the local relationships among intensity-levels, and in the second one we evaluated global visual appearance among physical scenes and tone-mapped images, which were presented side by side. We ranked the TMOs according
to how well they reproduced the results obtained in the physical scene. Our results show that ranking position clearly depends on the adopted evaluation criteria, which implies that, in general, these tone-mapping algorithms consider either local or global image attributes but rarely both. Regarding the
question of which TMO is the best, KimKautz [1] and Krawczyk [2] obtained the better results across the different experiments. We conclude that a more thorough and standardized evaluation criteria is needed to study all the characteristics of TMOs, as there is ample room for improvement in future developments. |
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NEUROBIT; 600.120; 600.128 |
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Admin @ si @ CPO2018 |
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3088 |
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Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez |
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Polyp Detection Benchmark in Colonoscopy Videos using GTCreator: A Novel Fully Configurable Tool for Easy and Fast Annotation of Image Databases |
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Conference Article |
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2018 |
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32nd International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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CARS |
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ISE; MV; 600.119 |
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Admin @ si @ BHM2018 |
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3089 |
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Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
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An overview of incremental feature extraction methods based on linear subspaces |
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2018 |
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Knowledge-Based Systems |
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KBS |
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145 |
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219-235 |
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With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
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0950-7051 |
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ADAS; 600.118 |
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Admin @ si @ DFH2018 |
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3090 |
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Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier |
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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos |
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2018 |
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Arxiv |
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Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. |
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HUPBA |
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no |
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Admin @ si @ JKS2018 |
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3095 |
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Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas |
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Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic |
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2018 |
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Jornades Imatge i Recerca |
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JIR |
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DAG; 600.084; 600.135; 601.338; 600.121; 600.129 |
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Admin @ si @ GRB2018 |
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3173 |
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Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou |
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The Robust Reading Competition Annotation and Evaluation Platform |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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61-66 |
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The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121 |
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KGR2018 |
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3103 |
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Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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End-to-end Driving via Conditional Imitation Learning |
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2018 |
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IEEE International Conference on Robotics and Automation |
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4693 - 4700 |
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Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL |
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Brisbane; Australia; May 2018 |
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ICRA |
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ADAS; 600.116; 600.124; 600.118 |
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Admin @ si @ CML2018 |
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3108 |
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Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel |
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A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees |
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2018 |
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15th International Conference on Image Analysis and Recognition |
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10882 |
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465-473 |
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Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. |
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Povoa de Varzim; Portugal; June 2018 |
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ICIAR |
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MILAB; no proj |
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Admin @ si @ SRR2018a |
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3110 |
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Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
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A picture is worth a thousand words but how to organize thousands of pictures? |
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2018 |
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Arxiv |
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We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
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Admin @ si @ LRD2018 |
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3111 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig |
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SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. |
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2018 |
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21st International Conference on Medical Image Computing & Computer Assisted Intervention |
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2 |
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21-29 |
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Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU. |
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Granada; Espanya; September 2018 |
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MICCAI |
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MILAB; no proj |
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no |
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Admin @ si @ SRA2018 |
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3112 |
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