| Records |
| Author |
Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
| Title |
Incremental model learning for spectroscopy-based food analysis |
Type |
Journal Article |
| Year |
2017 |
Publication |
Chemometrics and Intelligent Laboratory Systems |
Abbreviated Journal |
CILS |
| Volume |
167 |
Issue  |
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Pages |
123-131 |
| Keywords |
Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
| Abstract |
In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ DGK2017 |
Serial |
3002 |
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| Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
| Title |
Decremental generalized discriminative common vectors applied to images classification |
Type |
Journal Article |
| Year |
2017 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
131 |
Issue |
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Pages |
46-57 |
| Keywords |
Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
| Abstract |
In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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| Notes |
ADAS; 600.118; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DMH2017a |
Serial |
3003 |
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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 |
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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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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BGE2017 |
Serial |
3014 |
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| Author |
Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera |
| Title |
Error-Correcting Factorization |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
40 |
Issue |
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Pages |
2388-2401 |
| Keywords |
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| Abstract |
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. |
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| ISSN |
0162-8828 |
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| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BPT2018 |
Serial |
3015 |
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| Author |
Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva |
| Title |
Towards social pattern characterization from egocentric photo-streams |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
171 |
Issue |
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Pages |
104-117 |
| Keywords |
Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks |
| Abstract |
Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ADC2018 |
Serial |
3022 |
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| Author |
Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya |
| Title |
Increased Nucleus Accumbens Volume in First-Episode Psychosis |
Type |
Journal Article |
| Year |
2017 |
Publication |
Psychiatry Research-Neuroimaging |
Abbreviated Journal |
PRN |
| Volume |
263 |
Issue |
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Pages |
57-60 |
| Keywords |
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| Abstract |
Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. |
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| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ FSM2017 |
Serial |
3028 |
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| Author |
Debora Gil; Rosa Maria Ortiz; Carles Sanchez; Antoni Rosell |
| Title |
Objective endoscopic measurements of central airway stenosis. A pilot study |
Type |
Journal Article |
| Year |
2018 |
Publication |
Respiration |
Abbreviated Journal |
RES |
| Volume |
95 |
Issue |
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Pages |
63–69 |
| Keywords |
Bronchoscopy; Tracheal stenosis; Airway stenosis; Computer-assisted analysis |
| Abstract |
Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. Objective: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. Methods: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. Results: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. Conclusion: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI. |
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| Notes |
IAM; 600.075; 600.096; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ GOS2018 |
Serial |
3043 |
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| Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
| Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
| Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
145 |
Issue |
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Pages |
219-235 |
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With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
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0950-7051 |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ DFH2018 |
Serial |
3090 |
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| Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |
| Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
6 |
Issue |
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Pages |
18325 - 18334 |
| Keywords |
Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
| Abstract |
In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
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2169-3536 |
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| Notes |
ADAS; 600.118 |
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no |
| Call Number |
Admin @ si @ DMH2018b |
Serial |
3091 |
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| Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
| Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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80 |
Issue |
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Pages |
64-82 |
| Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
| Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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| Notes |
DAG; 600.097; 600.121 |
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no |
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Admin @ si @ RSJ2018 |
Serial |
3096 |
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| Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva |
| Title |
Egocentric video description based on temporally-linked sequences |
Type |
Journal Article |
| Year |
2018 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
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50 |
Issue |
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Pages |
205-216 |
| Keywords |
egocentric vision; video description; deep learning; multi-modal learning |
| Abstract |
Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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MILAB; no proj |
Approved |
no |
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Admin @ si @ BPC2018 |
Serial |
3109 |
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| Author |
Ivet Rafegas; Maria Vanrell |
| Title |
Color encoding in biologically-inspired convolutional neural networks |
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Journal Article |
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2018 |
Publication |
Vision Research |
Abbreviated Journal |
VR |
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151 |
Issue |
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Pages |
7-17 |
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Color coding; Computer vision; Deep learning; Convolutional neural networks |
| Abstract |
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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CIC; 600.051; 600.087 |
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no |
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Admin @ si @RaV2018 |
Serial |
3114 |
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Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez |
| Title |
Beyond Oneshot Encoding: lower dimensional target embedding |
Type |
Journal Article |
| Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
75 |
Issue |
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Pages |
21-31 |
| Keywords |
Error correcting output codes; Output embeddings; Deep learning; Computer vision |
| Abstract |
Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. |
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ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 |
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no |
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Admin @ si @ RBE2018 |
Serial |
3120 |
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Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari |
| Title |
Dominant and Complementary Emotion Recognition from Still Images of Faces |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
6 |
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Pages |
26391 - 26403 |
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| Abstract |
Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition. |
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HUPBA; no proj |
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no |
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Admin @ si @ GLW2018 |
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3122 |
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| Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
| Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
171 |
Issue |
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Pages |
118-139 |
| Keywords |
Human motion recognition; RGB-D data; Deep learning; Survey |
| Abstract |
Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. |
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HUPBA; no proj |
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no |
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Admin @ si @ WLO2018 |
Serial |
3123 |
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