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Author | David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri | ||||
Title | Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration | Type | Conference Article | ||
Year | 2007 | Publication | Computer Analysis of Images and Patterns, 12th International Conference | Abbreviated Journal | |
Volume | 4673 | Issue | Pages | 285–292 | |
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Address | Vienna (Austria) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-540-74271-5 | Medium | ||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ RRF2007b | Serial | 832 | ||
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Author | Laura Igual; Santiago Segui; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
Title | Eigenmotion-Based Detection of Intestinal Contractions | Type | Conference Article | ||
Year | 2007 | Publication | Computer Analysis of Images and Patterns, 12th International Conference | Abbreviated Journal | |
Volume | 4673 | Issue | Pages | 293–300 | |
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Address | Vienna (Austria) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-540-74271-5 | Medium | ||
Area | Expedition | Conference | CAIP | ||
Notes | OR;MILAB;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ ISV2007a | Serial | 895 | ||
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Author | Mehdi Mirza-Mohammadi; Sergio Escalera; Petia Radeva | ||||
Title | Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization | Type | Conference Article | ||
Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 5702 | Issue | Pages | 748–756 | |
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Abstract | Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | HuPBA; MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ MEP2009 | Serial | 1185 | ||
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Author | Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke | ||||
Title | Graph-based k-means clustering: A comparison of the set versus the generalized median graph | Type | Conference Article | ||
Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 5702 | Issue | Pages | 342–350 | |
Keywords | |||||
Abstract | In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. | ||||
Address | Münster, Germany | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ FVS2009d | Serial | 1219 | ||
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Author | Muhammad Anwer Rao; David Vazquez; Antonio Lopez | ||||
Title | Color Contribution to Part-Based Person Detection in Different Types of Scenarios | Type | Conference Article | ||
Year | 2011 | Publication | 14th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 6855 | Issue | II | Pages | 463-470 |
Keywords | Pedestrian Detection; Color | ||||
Abstract | Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. | ||||
Address | Seville, Spain | ||||
Corporate Author | Thesis | ||||
Publisher | Springer | Place of Publication | Berlin Heidelberg | Editor | P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
Language | English | Summary Language | english | Original Title | Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-23677-8 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ RVL2011b | Serial | 1665 | ||
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Author | Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta | ||||
Title | Structure-Preserving Smoothing of Biomedical Images | Type | Conference Article | ||
Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 5702 | Issue | Pages | 427-434 | |
Keywords | non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography. | ||||
Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
Address | Münster, Germany | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ GHB2009 | Serial | 1527 | ||
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Author | Petia Radeva; Enric Marti | ||||
Title | An improved model of snakes for model-based segmentation | Type | Conference Article | ||
Year | 1995 | Publication | Proceedings of Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | Issue | Pages | 515-520 | ||
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Abstract | The main advantage of segmentation by snakes consists in its ability to incorporate smoothness constraints on the detected shapes that can occur. Likewise, we propose to model snakes with other properties that reflect the information provided about the object of interest in a different extent. We consider different kinds of snakes, those searching for contours with a certain direction, those preserving an object’s model, those seeking for symmetry, those expanding open, etc. The availability of such a collection of snakes allows not only the more complete use of the knowledge about the segmented object, but also to solve some problems of the existing snakes. Our experiments on segmentation of facial features justify the usefulness of snakes with different properties. | ||||
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 | CAIP | ||
Notes | MILAB;IAM | Approved | no | ||
Call Number | IAM @ iam @ RaM1995b | Serial | 1632 | ||
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Author | Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie | ||||
Title | Inferring the Performance of Medical Imaging Algorithms | Type | Conference Article | ||
Year | 2011 | Publication | 14th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 6854 | Issue | Pages | 520-528 | |
Keywords | Validation, Statistical Inference, Medical Imaging Algorithms. | ||||
Abstract | Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence. |
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Address | Sevilla | ||||
Corporate Author | Thesis | ||||
Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Berlin | Editor | Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | L | Abbreviated Series Title | LNCS | |
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | IAM; ADAS | Approved | no | ||
Call Number | IAM @ iam @ HGR2011 | Serial | 1676 | ||
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Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Space Variant Representations for Mobile Platform Vision Applications | Type | Conference Article | ||
Year | 2011 | Publication | 14th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 6855 | Issue | II | Pages | 146-154 |
Keywords | |||||
Abstract | The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow. | ||||
Address | Seville, Spain | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-23677-8 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | ADAS | Approved | no | ||
Call Number | NaS2011; ADAS @ adas @ | Serial | 1686 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg | ||||
Title | Evaluating the impact of color on texture recognition | Type | Conference Article | ||
Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 8047 | Issue | Pages | 154-162 | |
Keywords | Color; Texture; image representation | ||||
Abstract | State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets. |
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Address | York; UK; August 2013 | ||||
Corporate Author | Thesis | ||||
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 | 0302-9743 | ISBN | 978-3-642-40260-9 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | CIC; 600.048 | Approved | no | ||
Call Number | Admin @ si @ KWA2013 | Serial | 2263 | ||
Permanent link to this record | |||||
Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario | Type | Conference Article | ||
Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 8048 | Issue | Pages | 483-490 | |
Keywords | Optical flow; regularization; Driver Assistance Systems; Performance Evaluation | ||||
Abstract | Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI). | ||||
Address | York; UK; August 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-40245-6 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | ADAS; 600.055; 601.215 | Approved | no | ||
Call Number | Admin @ si @ OnS2013b | Serial | 2244 | ||
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Author | Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo | ||||
Title | Multispectral Stereo Image Correspondence | Type | Conference Article | ||
Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 8048 | Issue | Pages | 217-224 | |
Keywords | |||||
Abstract | This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach. | ||||
Address | York; uk; August 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-40245-6 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | ADAS; 600.055 | Approved | no | ||
Call Number | Admin @ si @ PST2013 | Serial | 2561 | ||
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Author | Eduardo Aguilar; Petia Radeva | ||||
Title | Class-Conditional Data Augmentation Applied to Image Classification | Type | Conference Article | ||
Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 11679 | Issue | Pages | 182-192 | |
Keywords | CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition | ||||
Abstract | Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. | ||||
Address | Salermo; Italy; September 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ AgR2019 | Serial | 3366 | ||
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Author | Estefania Talavera; Nicolai Petkov; Petia Radeva | ||||
Title | Unsupervised Routine Discovery in Egocentric Photo-Streams | Type | Conference Article | ||
Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 11678 | Issue | Pages | 576-588 | |
Keywords | Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis | ||||
Abstract | The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people. | ||||
Address | Salermo; Italy; September 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ TPR2019a | Serial | 3367 | ||
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Author | Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer | ||||
Title | When Deep Learners Change Their Mind: Learning Dynamics for Active Learning | Type | Conference Article | ||
Year | 2021 | Publication | 19th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 13052 | Issue | 1 | Pages | 403-413 |
Keywords | |||||
Abstract | 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. | ||||
Address | September 2021 | ||||
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 | CAIP | ||
Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ ZRV2021 | Serial | 3673 | ||
Permanent link to this record |