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| 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 | 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  |
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| 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 | 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 |
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| Call Number | Admin @ si @ DMH2017a | Serial | 3003 | ||
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| Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
| Title | Learning to Learn from Web Data through Deep Semantic Embeddings | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 11134 | Issue | Pages | 514-529 | |
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| Abstract | In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. | ||||
| Address | Munich; Alemanya; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 600.129; 601.338; 600.121 | Approved |
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| Call Number | Admin @ si @ GGG2018a | Serial | 3175 | ||
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| Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny | ||||
| Title | Don't only Feel Read: Using Scene text to understand advertisements | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. | ||||
| Address | Salt Lake City; Utah; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | DAG; 600.121; 600.129 | Approved |
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| Call Number | Admin @ si @ DGV2018 | Serial | 3551 | ||
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| Author | Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo | ||||
| Title | Reading Text in the Wild from Compressed Images | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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| Abstract | Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV - EPIC | ||
| Notes | DAG; 600.084; 600.121 | Approved |
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| Call Number | Admin @ si @ GBS2017 | Serial | 3006 | ||
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| Author | Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai | ||||
| Title | WordFences: Text Localization and Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
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| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ PAE2017 | Serial | 3007 | ||
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| Author | Sergio Escalera; Vassilis Athitsos; Isabelle Guyon | ||||
| Title | Challenges in Multi-modal Gesture Recognition | Type | Book Chapter | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-60 | ||
| Keywords | Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM | ||||
| Abstract | This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. | ||||
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| Notes | HuPBA; no proj | Approved |
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| Call Number | Admin @ si @ EAG2017 | Serial | 3008 | ||
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| Author | Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats | ||||
| Title | Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription | Type | Journal | ||
| Year | 2017 | Publication | Journal of Medical Engineering & Technology | Abbreviated Journal | JMET |
| Volume | 41 | Issue | 6 | Pages | 486-497 |
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| Abstract | INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question. OBJECTIVES: To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination. METHODS: Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows. RESULTS: A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation). CONCLUSIONS: Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ EPB2017 | Serial | 3010 | ||
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| Author | Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Audio-Visual Emotion Recognition in Video Clips | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 10 | Issue | 1 | Pages | 60-75 |
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| Abstract | This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. | ||||
| Address | 1 Jan.-March 2019 | ||||
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| Notes | HUPBA; 602.143; 602.133 | Approved |
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| Call Number | Admin @ si @ NMN2017 | Serial | 3011 | ||
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| Author | Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People: A Review of Events and Resources | Type | Conference Article | ||
| Year | 2017 | Publication | 30th International Joint Conference on Neural Networks | Abbreviated Journal | |
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| Abstract | This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. | ||||
| Address | Anchorage; Alaska; USA; May 2017 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA; 602.143 | Approved |
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| Call Number | Admin @ si @ EBE2017 | Serial | 3012 | ||
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| Author | Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe | ||||
| Title | Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks. |
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| Address | Washington;USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no menciona | Approved |
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| Call Number | Admin @ si @ ATE2017 | Serial | 3013 | ||
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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 |
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| 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 | Pages | 2388-2401 | |
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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 | ISBN | Medium | ||
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| Notes | HuPBA; no menciona | Approved |
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| Call Number | Admin @ si @ BPT2018 | Serial | 3015 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th international conference on image analysis and processing | Abbreviated Journal | |
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| Keywords | CNN in Multispectral Imaging; Image Colorization | ||||
| Abstract | This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. | ||||
| Address | Catania; Italy; September 2017 | ||||
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| Area | Expedition | Conference | ICIAP | ||
| Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved |
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| Call Number | Admin @ si @ SSV2017c | Serial | 3016 | ||
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| Author | Meysam Madadi | ||||
| Title | Human Segmentation, Pose Estimation and Applications | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Automatic analyzing humans in photographs or videos has great potential applications in computer vision, including medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task.
In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. nonrigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures. |
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| Address | October 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Jordi Gonzalez | |
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| ISSN | ISBN | 978-84-945373-3-2 | Medium | ||
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| Notes | HUPBA | Approved |
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| Call Number | Admin @ si @ Mad2017 | Serial | 3017 | ||
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