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Author Luis Herranz; Weiqing Min; Shuqiang Jiang
Title Food recognition and recipe analysis: integrating visual content, context and external knowledge Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ HMJ2018 Serial 3250
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Author Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska
Title Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation Type Conference Article
Year 2018 Publication International MICCAI Brainlesion Workshop Abbreviated Journal
Volume 11384 Issue Pages 393-405
Keywords Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution
Abstract In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MICCAIW
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ PSH2018 Serial 3251
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Author Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua
Title Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST
Abstract Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ BRJ2018 Serial 3252
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Author Francisco Cruz; Oriol Ramos Terrades
Title A probabilistic framework for handwritten text line segmentation Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning
Abstract We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.
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Publisher (up) Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ CrR2018 Serial 3253
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Author Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone
Title DSD: document sparse-based denoising algorithm Type Journal Article
Year 2019 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 22 Issue 1 Pages 177–186
Keywords Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models
Abstract In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising.
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ DRT2019 Serial 3254
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Author Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez
Title System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture Type Patent
Year 2018 Publication US9946933B2 Abbreviated Journal
Volume Issue Pages
Keywords US9946933B2
Abstract A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ SGV2018 Serial 3255
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Author Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund
Title Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data Type Conference Article
Year 2019 Publication 14th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 123-130
Keywords Rain Removal; Traffic Surveillance; Image Denoising
Abstract Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance.
Address Praga; Czech Republic; February 2019
Corporate Author Thesis
Publisher (up) Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference VISIGRAPP
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ BVL2019 Serial 3256
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Author W.Win; B.Bao; Q.Xu; Luis Herranz; Shuqiang Jiang
Title Editorial Note: Efficient Multimedia Processing Methods and Applications Type Miscellaneous
Year 2019 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 78 Issue 1 Pages
Keywords
Abstract
Address
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Publisher (up) Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ WBX2019 Serial 3257
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Author Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan
Title Script independent approach for multi-oriented text detection in scene image Type Journal Article
Year 2017 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 242 Issue Pages 96-112
Keywords
Abstract Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability.
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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ DSR2017 Serial 3260
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Author Mikhail Mozerov; Fei Yang; Joost Van de Weijer
Title Sparse Data Interpolation Using the Geodesic Distance Affinity Space Type Journal Article
Year 2019 Publication IEEE Signal Processing Letters Abbreviated Journal SPL
Volume 26 Issue 6 Pages 943 - 947
Keywords
Abstract In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
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Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ MYW2019 Serial 3261
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Author Julio C. S. Jacques Junior; Cagri Ozcinar; Marina Marjanovic; Xavier Baro; Gholamreza Anbarjafari; Sergio Escalera
Title On the effect of age perception biases for real age regression Type Conference Article
Year 2019 Publication 14th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.
Address Lille; France; May 2019
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Area Expedition Conference FG
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ JOM2019 Serial 3262
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Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta
Title Bag of Negatives for Siamese Architectures Type Conference Article
Year 2019 Publication 30th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
Address Cardiff; United Kingdom; September 2019
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference BMVC
Notes CIC; 600.140; 600.118 Approved no
Call Number Admin @ si @ GAB2019b Serial 3263
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Author Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu
Title Saliency for fine-grained object recognition in domains with scarce training data Type Journal Article
Year 2019 Publication Pattern Recognition Abbreviated Journal PR
Volume 94 Issue Pages 62-73
Keywords
Abstract This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.
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Notes LAMP; OR; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ FGW2019 Serial 3264
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Author Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas
Title Selective Style Transfer for Text Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 805-812
Keywords transfer; text style transfer; data augmentation; scene text detection
Abstract This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
Address Sydney; Australia; September 2019
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Area Expedition Conference ICDAR
Notes DAG; 600.129; 600.135; 601.338; 601.310; 600.121 Approved no
Call Number GBG2019 Serial 3265
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Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas
Title Self-Supervised Learning from Web Data for Multimodal Retrieval Type Book Chapter
Year 2019 Publication Multi-Modal Scene Understanding Book Abbreviated Journal
Volume Issue Pages 279-306
Keywords self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding
Abstract Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, 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 proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art 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 basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen 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.
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Publisher (up) Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes DAG; 600.129; 601.338; 601.310 Approved no
Call Number Admin @ si @ GGG2019 Serial 3266
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