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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate edit   pdf
url  openurl
  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 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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  Area Expedition Conference  
  Notes ADAS; 600.118; 600.121;IAM Approved no  
  Call Number Admin @ si @ DMH2017a Serial 3003  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri edit   pdf
doi  openurl
  Title Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction Type Journal Article
  Year 2018 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 60 Issue 4 Pages 512-524  
  Keywords  
  Abstract This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.		  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129;IAM Approved no  
  Call Number Admin @ si @ DMH2018a Serial 3062  
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit   pdf
url  doi
openurl 
  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 Pages 219-235  
  Keywords  
  Abstract 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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  Corporate Author Thesis (up)  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0950-7051 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118;IAM 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 edit   pdf
doi  openurl
  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 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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  Corporate Author Thesis (up)  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2169-3536 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118;IAM Approved no  
  Call Number Admin @ si @ DMH2018b Serial 3091  
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma edit   pdf
url  openurl
  Title Scale coding bag of deep features for human attribute and action recognition Type Journal Article
  Year 2018 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 29 Issue 1 Pages 55-71  
  Keywords Action recognition; Attribute recognition; Bag of deep features  
  Abstract Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.  
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  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.068; 600.079; 600.106; 600.120;CIC;ADAS Approved no  
  Call Number Admin @ si @ KWR2018 Serial 3107  
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Author Joan Serrat; Felipe Lumbreras; Idoia Ruiz edit   pdf
url  openurl
  Title Learning to measure for preshipment garment sizing Type Journal Article
  Year 2018 Publication Measurement Abbreviated Journal MEASURE  
  Volume 130 Issue Pages 327-339  
  Keywords Apparel; Computer vision; Structured prediction; Regression  
  Abstract Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.  
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  Notes ADAS; MSIAU; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SLR2018 Serial 3128  
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Author Xavier Soria; Angel Sappa; Riad I. Hammoud edit   pdf
url  doi
openurl 
  Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
  Year 2018 Publication Sensors Abbreviated Journal SENS  
  Volume 18 Issue 7 Pages 2059  
  Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs  
  Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.		  
  Address  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SSH2018 Serial 3145  
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Author Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras edit  url
openurl 
  Title Segmentation of aerial images for plausible detail synthesis Type Journal Article
  Year 2018 Publication Computers & Graphics Abbreviated Journal CG  
  Volume 71 Issue Pages 23-34  
  Keywords Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation  
  Abstract The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.  
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  Series Volume Series Issue Edition  
  ISSN 0097-8493 ISBN Medium  
  Area Expedition Conference  
  Notes MSIAU; 600.086; 600.118;ADAS Approved no  
  Call Number Admin @ si @ ACC2018 Serial 3147  
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen edit   pdf
url  openurl
  Title Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
  Year 2018 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J  
  Volume 138 Issue Pages 74-85  
  Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis  
  Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.109; 600.106; 600.120;CIC;ADAS Approved no  
  Call Number Admin @ si @ RKW2018 Serial 3158  
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Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit   pdf
doi  openurl
  Title Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.  
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  Corporate Author Thesis (up)  
  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  
  Notes DAG; ADAS; 600.084; 600.118; 600.121; 600.129;IAM Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
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