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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
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Title |
An Approximate Algorith for Median Graph Computation using Graph Embedding |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition. |
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Tampa, USA |
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DAG @ dag @ FVS2008a |
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1064 |
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Author |
Murad Al Haj; Francisco Javier Orozco; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Automatic Face and Facial Features Initialization for Robust and Accurate Tracking |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition. |
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1– 4 |
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Tampa (Florida) |
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ISE @ ise @ AOG2008 |
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1072 |
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Author |
Ariel Amato; Mikhail Mozerov; Ivan Huerta; Jordi Gonzalez; Juan J. Villanueva |
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Title |
ackground Subtraction Technique Based on Chromaticity and Intensity Patterns |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition, |
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1–4 |
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Tampa (Florida) |
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ISE |
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ISE @ ise @ AMH2008 |
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1071 |
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Author |
Dimosthenis Karatzas; Marçal Rusiñol; Coen Antens; Miquel Ferrer |
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Title |
Segmentation Robust to the Vignette Effect for Machine Vision Systems |
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Conference Article |
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Year |
2008 |
Publication |
19th International Conference on Pattern Recognition |
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The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect. |
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Tampa, USA |
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DAG |
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no |
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DAG @ dag @ KRA2008 |
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1065 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; F. Kimura |
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Title |
Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition |
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Tampa (Florida) |
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DAG |
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no |
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DAG @ dag @ RPL2008d |
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1073 |
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Author |
H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent |
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Title |
Feature Selection Combining Genetic Algorithm and Adaboost Classifiers |
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Conference Article |
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2008 |
Publication |
19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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DAG |
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no |
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Admin @ si @ CRT2008 |
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1872 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades; S. Barrat |
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Title |
Histogram of radon transform. A useful descriptor for shape retrieval |
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Conference Article |
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Year |
2008 |
Publication |
19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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DAG |
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no |
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Admin @ si @ TRB2008 |
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1876 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition |
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Conference Article |
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Year |
2017 |
Publication |
19th International Conference on Multimodal Interaction |
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Keywords |
Convolutional Neural Networks; Texture Recognition; Local Binary Paterns |
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Abstract |
Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets. |
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Glasgow; Scothland; November 2017 |
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ACM |
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LAMP; 600.109; 600.068; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ RKW2017 |
Serial |
3038 |
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Permanent link to this record |
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Author |
Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Stable Anatomical Structure Tracking for video-bronchoscopy Navigation |
Type |
Conference Article |
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Year |
2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
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Keywords |
Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking |
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Abstract |
Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at dierent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. |
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Athens; Greece; October 2016 |
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MICCAIW |
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IAM; 600.075 |
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no |
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Call Number |
Admin @ si @ LSB2016b |
Serial |
2857 |
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Author |
Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell |
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Title |
Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches |
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Conference Article |
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Year |
2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
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Volume |
9401 |
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62-70 |
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Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy |
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Abstract |
Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. |
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Quebec; Canada; September 2016 |
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MICCAIW |
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IAM; MV; 600.060; 600.075 |
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no |
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Call Number |
Admin @ si @ SGB2016 |
Serial |
2885 |
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Author |
Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva |
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Title |
Learning the Lumen Border using a Convolutional Neural Networks classifier |
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Conference Article |
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2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop |
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IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows. |
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Athens; Greece; October 2016 |
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MICCAIW |
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MILAB; |
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no |
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Admin @ si @ MBB2016 |
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2822 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture |
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Conference Article |
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Year |
2017 |
Publication |
19th international conference on image analysis and processing |
Abbreviated Journal |
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CNN in Multispectral Imaging; Image Colorization |
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This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Catania; Italy; September 2017 |
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ICIAP |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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no |
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Admin @ si @ SSV2017c |
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3016 |
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Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca |
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Title |
Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image |
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Conference Article |
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2009 |
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19th International Conference on Computer Graphics and Vision |
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69–73 |
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In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching. |
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Moscow (Russia) |
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978-5-317-02975-3 |
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GRAPHICON |
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ISE |
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ISE @ ise @ MAR2009b |
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1207 |
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Author |
Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer |
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Title |
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning |
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Conference Article |
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Year |
2021 |
Publication |
19th International Conference on Computer Analysis of Images and Patterns |
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13052 |
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1 |
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403-413 |
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Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results. |
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September 2021 |
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CAIP |
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LAMP; |
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no |
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Admin @ si @ ZRV2021 |
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3673 |
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Author |
G. de Oliveira; Mariella Dimiccoli; Petia Radeva |
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Title |
Egocentric Image Retrieval With Deep Convolutional Neural Networks |
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Conference Article |
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Year |
2016 |
Publication |
19th International Conference of the Catalan Association for Artificial Intelligence |
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71-76 |
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Address |
Barcelona; Spain; October 2016 |
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CCIA |
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Notes |
MILAB |
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no |
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Call Number |
Admin @ si @ODR2016 |
Serial |
2790 |
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