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Machine Vision and Applications

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23-06-2018 | Editorial

Machine learning for big visual analysis

Authors: Jun Yu, Xue Mei, Fatih Porikli, Jason Corso

Published in: Machine Vision and Applications | Issue 6/2018

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With the proliferation of smartphones and handheld devices, the amount of visual data in our world has been exploding. Visual data have become so large and complex that the traditional data processing applications are inadequate to deal with it. As a result, the emergence of big visual data has brought a paradigm shift to many fields of visual data analysis. Visual data are not only big in volume and size, but also can be unstructured, incomplete, noisy, redundant, and heterogeneous. A variety of methods have been developed for addressing numerous challenging problems. However, many problems remain challenging especially on the scalability of computationally complex algorithms, the shortage of accurately annotated raw data, the issue of integrating heterogeneous data from different sources, the difficulty in discovering valuable knowledge from noisy and redundant data. …

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Title: Machine learning for big visual analysis
Authors: Jun Yu
Xue Mei
Fatih Porikli
Jason Corso
Publication date: 23-06-2018
Publisher: Springer Berlin Heidelberg
Published in: Machine Vision and Applications / Issue 6/2018
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-018-0948-5

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