| Records |
| Author |
Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo |
Title  |
Unsupervised scene adaptation for faster multi- scale pedestrian detection |
Type |
Conference Article |
| Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3534 - 3539 |
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| Address |
Stockholm; Sweden; August 2014 |
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Conference |
ICPR |
| Notes |
LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ BLK2014 |
Serial |
2519 |
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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 |
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| Volume |
11678 |
Issue |
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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 |
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LNCS |
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Conference |
CAIP |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ TPR2019a |
Serial |
3367 |
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| Author |
Daniel Ponsa; Xavier Roca |
| Title |
Unsupervised Parameterisation of Gaussian Mixture Models |
Type |
Miscellaneous |
| Year |
2002 |
Publication |
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ADAS;ISE |
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no |
| Call Number |
ADAS @ adas @ PoR2002c |
Serial |
313 |
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| Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
| Title |
Unsupervised Motion Classification by means of Efficient Feature Selection and Tracking |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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no |
| Call Number |
ADAS @ adas @ SAM2004a |
Serial |
456 |
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| Author |
Miguel Oliveira; Angel Sappa; V.Santos |
| Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
201-208 |
| Keywords |
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| Abstract |
The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
| Address |
Colorado Springs |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4577-0394-2 |
Medium |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OSS2011; ADAS @ adas @ |
Serial |
1766 |
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| Author |
David Guillamet; Jordi Vitria |
| Title |
Unsupervised Learning of Structural Object Representations |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 2:73–78 |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ GuV2001b |
Serial |
102 |
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| Author |
David Guillamet; Jordi Vitria |
| Title |
Unsupervised Learning of Part-Based Representations |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings 9th International Conference CAIP 2001,700–708, W. Skarbek (Ed.): Computer Analysis of Images and Patterns, LNCS 2059, Springer Verlag, 123–134. |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ GVi2001b |
Serial |
106 |
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| Author |
Eduard Vazquez |
| Title |
Unsupervised image segmentation based on material reflectance description and saliency |
Type |
Book Whole |
| Year |
2011 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Issue |
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| Abstract |
Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show |
| Address |
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| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
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Place of Publication |
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Editor |
Ramon Baldrich |
| Language |
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| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ Vaz2011b |
Serial |
1835 |
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| Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz |
| Title |
Unsupervised Domain Adaptation without Source Data by Casting a BAIT |
Type |
Miscellaneous |
| Year |
2020 |
Publication |
Arxiv |
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| Abstract |
arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ YWW2020 |
Serial |
3539 |
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| Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
| Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3492 - 3495 |
| Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
| Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
| Address |
Tsukuba Science City, Japan |
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| Publisher |
IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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ICPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction Of Hyperspectral Images |
Type |
Conference Article |
| Year |
2014 |
Publication |
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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| Keywords |
Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification |
| Abstract |
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. |
| Address |
Lausanne; Switzerland; June 2014 |
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WHISPERS |
| Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ RGC2014 |
Serial |
2513 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
| Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
| Keywords |
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| Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
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0196-2892 |
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| Notes |
LAMP; 600.079;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
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| Author |
Angel Sappa |
| Title |
Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation |
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Journal |
| Year |
2006 |
Publication |
IEEE Transactions on Image Processing, 15(2):377–384 |
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ADAS |
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no |
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ADAS @ adas @ Sap2006a |
Serial |
637 |
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| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
| Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
| Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
749-756 |
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Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
| Address |
Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
ISBN |
978-1-4673-1226-4 |
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CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RSL2012b; ADAS @ adas @ |
Serial |
2033 |
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| Author |
Huamin Ren; Weifeng Liu; Soren Ingvor Olsen; Sergio Escalera; Thomas B. Moeslund |
| Title |
Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
26th British Machine Vision Conference |
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| Address |
Swansea; uk; September 2015 |
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BMVC |
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RLO2015 |
Serial |
2658 |
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