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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Embedding of Graphs with Discrete Attributes Via Label Frequencies Type Journal Article
Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 27 Issue 3 Pages (down) 1360002-1360029
Keywords Discrete attributed graphs; graph embedding; graph classification
Abstract Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient.
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 DAG Approved no
Call Number Admin @ si @ GVB2013 Serial 2305
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Author Santiago Segui; Laura Igual; Jordi Vitria
Title Bagged One Class Classifiers in the Presence of Outliers Type Journal Article
Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 27 Issue 5 Pages (down) 1350014-1350035
Keywords One-class Classifier; Ensemble Methods; Bagging and Outliers
Abstract The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way.
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 OR; 600.046;MV Approved no
Call Number Admin @ si @ SIV2013 Serial 2256
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Author Olivier Penacchio; Xavier Otazu; Laura Dempere-Marco
Title A Neurodynamical Model of Brightness Induction in V1 Type Journal Article
Year 2013 Publication PloS ONE Abbreviated Journal Plos
Volume 8 Issue 5 Pages (down) e64086
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.
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 CIC Approved no
Call Number Admin @ si @ POD2013 Serial 2242
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Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames Type Conference Article
Year 2013 Publication 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal
Volume Issue Pages (down) 7350 - 7354
Keywords
Abstract In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results.
Address Osaka; Japan; July 2013
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 1557-170X ISBN Medium
Area 800 Expedition Conference EMBC
Notes MV; 600.047; 600.060;SIAI Approved no
Call Number Admin @ si @ BSV2013 Serial 2286
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Author J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin
Title Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm Type Journal Article
Year 2013 Publication Expert Systems with Applications Abbreviated Journal EXWA
Volume 40 Issue 17 Pages (down) 6707-6712
Keywords Neural gas; Expert vision; Eye-tracking; Fixations
Abstract Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.
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 0957-4174 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ CRM2013 Serial 2438
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Author German Ros; J. Guerrero; Angel Sappa; Antonio Lopez
Title VSLAM pose initialization via Lie groups and Lie algebras optimization Type Conference Article
Year 2013 Publication Proceedings of IEEE International Conference on Robotics and Automation Abbreviated Journal
Volume Issue Pages (down) 5740 - 5747
Keywords SLAM
Abstract We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.
Address Karlsruhe; Germany; May 2013
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 1050-4729 ISBN 978-1-4673-5641-1 Medium
Area Expedition Conference ICRA
Notes ADAS; 600.054; 600.055; 600.057 Approved no
Call Number Admin @ si @ RGS2013a; ADAS @ adas @ Serial 2225
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Author Mohammad Rouhani; Angel Sappa
Title The Richer Representation the Better Registration Type Journal Article
Year 2013 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 22 Issue 12 Pages (down) 5036-5049
Keywords
Abstract In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach.
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 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RoS2013 Serial 2665
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Author Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat
Title Discriminative Color Descriptors Type Conference Article
Year 2013 Publication IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages (down) 2866 - 2873
Keywords
Abstract Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.
Address Portland; Oregon; June 2013
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 1063-6919 ISBN Medium
Area Expedition Conference CVPR
Notes CIC; 600.048 Approved no
Call Number Admin @ si @ KWK2013a Serial 2262
Permanent link to this record
 
 
Author Ivo Everts; Jan van Gemert; Theo Gevers
Title Evaluation of Color STIPs for Human Action Recognition Type Conference Article
Year 2013 Publication IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages (down) 2850-2857
Keywords
Abstract This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.
Address Portland; oregon; June 2013
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 1063-6919 ISBN Medium
Area Expedition Conference CVPR
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ EGG2013 Serial 2364
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding Type Journal Article
Year 2013 Publication Pattern Recognition Abbreviated Journal PR
Volume 46 Issue 10 Pages (down) 2830-2839
Keywords Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification
Abstract Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2013a Serial 2247
Permanent link to this record
 
 
Author Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga
Title Low-level SpatioChromatic Grouping for Saliency Estimation Type Journal Article
Year 2013 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 35 Issue 11 Pages (down) 2810-2816
Keywords
Abstract We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics.
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 CIC; 600.051; 600.052; 605.203 Approved no
Call Number Admin @ si @ MVO2013 Serial 2289
Permanent link to this record
 
 
Author Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe
Title Random Forests of Local Experts for Pedestrian Detection Type Conference Article
Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages (down) 2592 - 2599
Keywords ADAS; Random Forest; Pedestrian Detection
Abstract Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.
Address Sydney; Australia; December 2013
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5499 ISBN Medium
Area Expedition Conference ICCV
Notes ADAS; 600.057; 600.054 Approved no
Call Number ADAS @ adas @ MVL2013 Serial 2333
Permanent link to this record
 
 
Author Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool
Title Active MAP Inference in CRFs for Efficient Semantic Segmentation Type Conference Article
Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages (down) 2312 - 2319
Keywords Semantic Segmentation
Abstract Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
Address Sydney; Australia; December 2013
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 1550-5499 ISBN Medium
Area Expedition Conference ICCV
Notes ADAS; 600.057 Approved no
Call Number ADAS @ adas @ RBN2013 Serial 2377
Permanent link to this record
 
 
Author Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet
Title Towards multispectral data acquisition with hand-held devices Type Conference Article
Year 2013 Publication 20th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages (down) 2053 - 2057
Keywords Multispectral; mobile devices; color measurements
Abstract We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic.
Address Melbourne; Australia; September 2013
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 ICIP
Notes CIC; DAG; 600.048 Approved no
Call Number Admin @ si @ KWK2013b Serial 2265
Permanent link to this record
 
 
Author Mikhail Mozerov
Title Constrained Optical Flow Estimation as a Matching Problem Type Journal Article
Year 2013 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 22 Issue 5 Pages (down) 2044-2055
Keywords
Abstract In general, discretization in the motion vector domain yields an intractable number of labels. In this paper we propose an approach that can reduce general optical flow to the constrained matching problem by pre-estimating a 2D disparity labeling map of the desired discrete motion vector function. One of the goals of the proposed paper is estimating coarse distribution of motion vectors and then utilizing this distribution as global constraints for discrete optical flow estimation. This pre-estimation is done with a simple frame-to-frame correlation technique also known as the digital symmetric-phase-only-filter (SPOF). We discover a strong correlation between the output of the SPOF and the motion vector distribution of the related optical flow. The two step matching paradigm for optical flow estimation is applied: pixel accuracy (integer flow), and subpixel accuracy estimation. The matching problem is solved by global optimization. Experiments on the Middlebury optical flow datasets confirm our intuitive assumptions about strong correlation between motion vector distribution of optical flow and maximal peaks of SPOF outputs. The overall performance of the proposed method is promising and achieves state-of-the-art results on the Middlebury benchmark.
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 1057-7149 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ Moz2013 Serial 2191
Permanent link to this record