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arXiv 2021年

作者： Petersen, Jens Isensee, Fabian Köhler, Gregor Jäger, Paul F. Zimmerer, David Neuberger, Ulf Wick, Wolfgang Debus, Jürgen Heiland, Sabine Bendszus, Martin Vollmuth, Philipp Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIP Applied Computer Vision Lab Division of Medical Image Computing German Cancer Research Center Germany Interactive Machine Learning Group German Cancer Research Center Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Neurology Clinic Heidelberg University Hospital Germany DKTK CCU Neurooncology German Cancer Research Center Germany Heidelberg Germany Heidelberg University Hospital Germany Clinical Cooperation Unit Radiation Oncology German Cancer Research Center Germany

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. The result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models. © 2021, CC BY-SA.

关键词： Tumors

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An evaluation of synthetic data for deep learning stereo depth algorithms 2017

An evaluation of synthetic data for deep learning stereo dep...

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2017 International Conference on Watermarking and Image Processing, ICWIP 2017

作者： Lee, Kevin Moloney, David Computer Vision and Machine Learning Group Intel/Movidius United States

ISBN: (纸本)9781450353076

Stereo vision is a very active field in the realm of computer vision and in recent years Convolutional Neural Networks (CNNs) have proven to be very competitive against the state-of-the-art. However, the performance of these networks are limited by the quality of the data that is used when training the CNNs. Data acquisition of high quality labeled images is a time-consuming and expensive process. By exploiting the power of modern-day powerful GPUs, we present a synthetic data-set with fully rectified stereo image pairs and accompanying accurate ground truth information that can be used for training and testing stereo algorithms. We provide validation of the quality of our dataset by performing quantitative experiments that suggest pre-training deep learning algorithms on synthetic data can perform competitively against networks trained on real life data. Testing on a real-world data-set, we found the accuracy performance difference between the real and synthetically trained networks was within a margin of 1.8%. We also illustrate the functionality synthetic data can provide, by conducting a key performance index on a selection of conventional and deep learning stereo algorithms available on embedded platforms and compared them under common metrics. © 2017 Association for Computing machinery.

关键词： Stereo vision

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

arXiv

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arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Applications of the VOLA Format for 3D Data Knowledge Discovery.

Applications of the VOLA Format for 3D Data Knowledge Discov...

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International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Jonathan Byrne Sam Caulfield Léonie Buckley Xiaofan Xu Dexmont Pena Gary Baugh David Moloney Computer Vision and Machine Learning Group Movidius / Intel

VOLA is a compact data structure that unifies computer vision and 3D rendering and allows for the rapid calculation of connected components, per-voxel census/accounting, CNN inference, path planning and obstacle avoidance. Using a hierarchical bit array format allows it to run efficiently on embedded systems and maximize the level of data compression. The proposed format allows massive scale volumetric data to be used in embedded applications where it would be inconceivable to utilize point-clouds due to memory constraints. Furthermore, geographical and qualitative data is embedded in the file structure to allow it to be used in place of standard point cloud formats. This work examines the reduction in file size when encoding 3D data using the VOLA format and finds that it is an order of magnitude smaller than the current binary standard for point cloud data.

关键词： Point Clouds Embedded Systems Auralisation Deep learning GIS

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Cross-dimensional weighting for aggregated deep convolutional features 14

Cross-dimensional weighting for aggregated deep convolutiona...

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computer vision - ECCV 2016 Workshops, Proceedings

作者： Kalantidis, Yannis Mellina, Clayton Osindero, Simon Computer Vision and Machine Learning Group Flickr Yahoo San Francisco United States

ISBN: (纸本)9783319466033

We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific non-parametric schemes for both spatial-and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects. We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks. We also provide an easy-to-use, open source implementation that reproduces our results. © Springer International Publishing Switzerland 2016.

关键词： Network layers

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

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Why is the winner the best?

arXiv

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arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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Click-Free, Video-Based Document Capture - Methodology and Evaluation

Click-Free, Video-Based Document Capture - Methodology and E...

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International Conference on Document Analysis and Recognition

作者： Waqas Tariq Nazar Khan Computer Vision & Machine Learning Group Punjab University College of Information Technology Lahore Pakistan

We propose a click-free method for video-based digitization of multi-page documents. The work is targeted at the non-commercial, low-volume, home user. The document is viewed through a mounted camera and the user is only required to turn pages manually while the system automatically extracts the video frames representing stationary document pages. This is in contrast to traditional document conversion approaches such as photocopying and scanning which can be time-consuming, repetitive, redundant and can lead to document deterioration. Main contributions of our work are i) a 3-step method for automatic extraction of unique, stable and clear document pages from video, ii) a manually annotated data set of 37 videos consisting of 763 page turn events covering a large variety of documents, and iii) a soft, quantitative evaluation criterion that is highly correlated with the hard F 1 -measure. The criterion is motivated by the need to counter the subjectivity in human marked ground truth for videos. On our data set, we report an F 1 -measure of 0.91 and a soft score of 0.94 for the page extraction task.

关键词： Cameras Task analysis Standards Detectors Text analysis Optical distortion

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LOH and behold: Web-scale visual search, recommendation and clustering using locally optimized hashing 14

LOH and behold: Web-scale visual search, recommendation and ...

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computer vision - ECCV 2016 Workshops, Proceedings

作者： Kalantidis, Yannis Kennedy, Lyndon Nguyen, Huy Mellina, Clayton Shamma, David A. Computer Vision and Machine Learning Group Flickr Yahoo San Francisco United States Futurewei Technologies Inc Santa Clara United States CWI: Centrum Wiskunde and Informatica Amsterdam Netherlands

ISBN: (纸本)9783319466033

We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scale search, recommendation, clustering, and deduplication. We show that matching with LOH only requires set intersections and summations to compute and so is easily implemented in generic distributed computing systems. We further show application of LOH to: (a) large-scale search tasks where performance is on par with other state-of-the-art hashing approaches (b) large-scale recommendation where queries consisting of thousands of images can be used to generate accurate recommendations from collections of hundreds of millions of images;and (c) efficient clustering with a graph-based algorithm that can be scaled to massive collections in a distributed environment or can be used for deduplication for small collections, like search results, performing better than traditional hashing approaches while only requiring a few milliseconds to run. In this paper we experiment on datasets of up to 100 million images, but in practice our system can scale to larger collections and can be used for other types of data that have a vector representation in a Euclidean space. © Springer International Publishing Switzerland 2016.

关键词： Vector spaces

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