| Records |
| Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen |
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 |
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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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| Notes |
LAMP; 600.109; 600.106; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ RKW2018 |
Serial |
3158 |
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| Author |
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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| Notes |
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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| Author |
Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga |
| Title |
Beyond Eleven Color Names for Image Understanding |
Type |
Journal Article |
| Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
29 |
Issue |
2 |
Pages |
361-373 |
| Keywords |
Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
| Abstract |
Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. |
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LAMP; NEUROBIT; 600.068; 600.109; 600.120 |
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no |
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Admin @ si @ YYW2018 |
Serial |
3087 |
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| Author |
Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
| Title |
Batch-based activity recognition from egocentric photo-streams revisited |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
| Volume |
21 |
Issue |
4 |
Pages |
953–965 |
| Keywords |
Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
| Abstract |
Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
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MILAB; no proj |
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no |
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Admin @ si @ CMR2018 |
Serial |
3186 |
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Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund |
| Title |
Back-dropout Transfer Learning for Action Recognition |
Type |
Journal Article |
| Year |
2018 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
| Volume |
12 |
Issue |
4 |
Pages |
484-491 |
| Keywords |
Learning (artificial intelligence); Pattern Recognition |
| Abstract |
Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKM2018 |
Serial |
3071 |
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| Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
| Title |
Back to Front Architecture for Diagnosis as a Service |
Type |
Conference Article |
| Year |
2018 |
Publication |
20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
Abbreviated Journal |
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Issue |
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Pages |
343-346 |
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Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. |
| Address |
Timisoara; Rumania; September 2018 |
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SYNASC |
| Notes |
IAM; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ SVA2018 |
Serial |
3360 |
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| Author |
Marçal Rusiñol; Lluis Gomez |
| Title |
Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos |
Type |
Journal |
| Year |
2018 |
Publication |
Revista anual de la Asociación de Archiveros de Castilla y León |
Abbreviated Journal |
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| Volume |
21 |
Issue |
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Pages |
161-174 |
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| Notes |
DAG; 600.121; 600.129 |
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no |
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Admin @ si @ RuG2018 |
Serial |
3239 |
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Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE Winter Applications of Computer Vision Workshops |
Abbreviated Journal |
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Issue |
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Pages |
28-36 |
| Keywords |
IEEE Winter Applications of Computer Vision Workshops |
| Abstract |
Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. |
| Address |
Lake Tahoe; USA; March 2018 |
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WACVW |
| Notes |
HUPBA; 602.133 |
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no |
| Call Number |
Admin @ si @ JBA2018 |
Serial |
3121 |
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| Author |
Marçal Rusiñol; J. Chazalon; Katerine Diaz |
| Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
Type |
Journal Article |
| Year |
2018 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
77 |
Issue |
11 |
Pages |
13773-13798 |
| Keywords |
Augmented reality; Document image matching; Educational applications |
| Abstract |
This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
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DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
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no |
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Admin @ si @ RCD2018 |
Serial |
2996 |
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Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez |
| Title |
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
11212 |
Issue |
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Pages |
357-372 |
| Keywords |
Deep Learning; Convolutional Neural Networks; Attention |
| Abstract |
We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100. |
| Address |
Munich; September 2018 |
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LNCS |
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ECCV |
| Notes |
ISE; 600.098; 602.121; 600.119 |
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no |
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Admin @ si @ RGC2018 |
Serial |
3139 |
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Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
| Title |
Articulated Motion and Deformable Objects |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
79 |
Issue |
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Pages |
55-64 |
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| Abstract |
This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
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HUPBA; no proj |
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no |
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Admin @ si @ WEP2018 |
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3126 |
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Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
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An overview of incremental feature extraction methods based on linear subspaces |
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Journal Article |
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2018 |
Publication |
Knowledge-Based Systems |
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KBS |
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145 |
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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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ADAS; 600.118 |
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no |
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Admin @ si @ DFH2018 |
Serial |
3090 |
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Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
| Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
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Conference Article |
| Year |
2018 |
Publication |
14th Asian Conference on Computer Vision |
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In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
| Address |
Perth; Australia; December 2018 |
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ACCV |
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DAG; 600.097; 600.121; 600.129 |
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no |
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Admin @ si @ DDG2018a |
Serial |
3151 |
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| Author |
Cesar de Souza |
| Title |
Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video |
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Book Whole |
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2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
| Address |
April 2018 |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;Naila Murray |
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ADAS; 600.118 |
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no |
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Admin @ si @ Sou2018 |
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3127 |
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| Author |
Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera |
| Title |
Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly |
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Journal Article |
| Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
29 |
Issue |
5 |
Pages |
765–788 |
| Keywords |
Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology |
| Abstract |
We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach. |
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HUPBA; no proj |
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no |
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Admin @ si @ CPP2018 |
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3125 |
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