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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad | ||||
| Title | Locality Regularized Group Sparse Coding for Action Recognition | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 158 | Issue | Pages | 106-114 | |
| Keywords | Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition | ||||
Abstract  |
Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BGE2017 | Serial | 3014 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
| Title | Modulating Shape Features by Color Attention for Object Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 98 | Issue | 1 | Pages | 49-64 |
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| Abstract |
Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ KWV2012 | Serial | 1864 | ||
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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. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| 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 | Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski | ||||
| Title | Bayesian Flow Networks in Continual Learning | Type | Miscellaneous | ||
| Year | 2023 | Publication | arxiv | Abbreviated Journal | |
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| Abstract |
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ PDT2023 | Serial | 3972 | ||
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| Author | Xinhang Song; Shuqiang Jiang; Luis Herranz | ||||
| Title | Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 26 | Issue | 6 | Pages | 2721-2735 |
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| Abstract |
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SJH2017a | Serial | 2963 | ||
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| Author | Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | RGB-D Segmentation of Poultry Entrails | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
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| Abstract |
Best commercial paper award. | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PJM2016 | Serial | 2848 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Document noise removal using sparse representations over learned dictionary | Type | Conference Article | ||
| Year | 2013 | Publication | Symposium on Document engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 161-168 | ||
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| Abstract |
best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art. |
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| Address | Barcelona; October 2013 | ||||
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| ISSN | ISBN | 978-1-4503-1789-4 | Medium | ||
| Area | Expedition | Conference | ACM-DocEng | ||
| Notes | DAG; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ DTR2013a | Serial | 2330 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers | Type | Conference Article | ||
| Year | 2013 | Publication | 26th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | 7884 | Issue | Pages | 1-12 | |
| Keywords | Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature | ||||
| Abstract |
Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology. |
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| Address | Canada; May 2013 | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-38456-1 | Medium | |
| Area | Expedition | Conference | AI | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2013b | Serial | 2249 | ||
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| Author | E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara | ||||
| Title | Benchmarking Keypoint Filtering Approaches for Document Image Matching | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract |
Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial not only to processing speed but also to accuracy. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RCR2017 | Serial | 3000 | ||
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| Author | Victor Borjas; Jordi Vitria; Petia Radeva | ||||
| Title | Gradient Histogram Background Modeling for People Detection in Stationary Camera Environments | Type | Conference Article | ||
| Year | 2013 | Publication | 13th IAPR Conference on Machine Vision Applications | Abbreviated Journal | |
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| Abstract |
Best Poster AwardOne of the big challenges of today person detectors is the decreasing of the false positive rate. In this paper, we propose a novel framework to customize person detectors in static camera scenarios in order to reduce this rate. This scheme includes background modeling for subtraction based on gradient histograms and Mean-Shift clustering. Our experiments show that the detection improved compared to using only the output from the pedestrian detector reducing 87% of the false positives and therefore the overall precision of the detection
was increased signi cantly. |
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| Address | Kyoto; Japan; May 2013 | ||||
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| Area | Expedition | Conference | MVA | ||
| Notes | OR; MILAB;MV | Approved | no | ||
| Call Number | BVR2013 | Serial | 2238 | ||
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| Author | Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | Spatiotemporal Facial Super-Pixels for Pain Detection | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Facial images; Super-pixels; Spatiotemporal filters; Pain detection | ||||
| Abstract |
Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
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| Address | Palma de Mallorca; Spain; July 2016 | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LNM2016 | Serial | 2847 | ||
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| Author | Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li | ||||
| Title | Enhanced Asymmetric Bilinear Model for Face Recognition | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Distributed Sensor Networks | Abbreviated Journal | IJDSN |
| Volume | Issue | Pages | Article ID 218514 | ||
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| Abstract |
Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. | ||||
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| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GZG2015 | Serial | 2592 | ||
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| Author | David Rotger; Petia Radeva; N. Bruining | ||||
| Title | Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images using a Cascade of Classifiers | Type | Journal Article | ||
| Year | 2010 | Publication | IEEE Transactions on Information Technology in Biomedicine | Abbreviated Journal | TITB |
| Volume | 14 | Issue | 2 | Pages | 535 – 537 |
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| Abstract |
Bioabsorbable drug-eluting coronary stents present a very promising improvement to the common metallic ones solving some of the most important problems of stent implantation: the late restenosis. These stents made of poly-L-lactic acid cause a very subtle acoustic shadow (compared to the metallic ones) making difficult the automatic detection and measurements in images. In this paper, we propose a novel approach based on a cascade of GentleBoost classifiers to detect the stent struts using structural features to code the information of the different subregions of the struts. A stochastic gradient descent method is applied to optimize the overall performance of the detector. Validation results of struts detection are very encouraging with an average F-measure of 81%. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RRB2010 | Serial | 1287 | ||
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| Author | Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras | ||||
| Title | Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Intelligent and Robotic Systems | Abbreviated Journal | JIRC |
| Volume | 64 | Issue | 3-4 | Pages | 625-649 |
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| Abstract |
Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| ISSN | 0921-0296 | ISBN | Medium | ||
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| Notes | RV;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RGA2011 | Serial | 1728 | ||
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| Author | Joan Arnedo-Moreno; Agata Lapedriza | ||||
| Title | Visualizing key authenticity: turning your face into your public key | Type | Conference Article | ||
| Year | 2010 | Publication | 6th China International Conference on Information Security and Cryptology | Abbreviated Journal | |
| Volume | Issue | Pages | 605-618 | ||
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| Abstract |
Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | Inscrypt | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ArL2010c | Serial | 2149 | ||
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