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Semi-supervised learning on large-scale geotagged photos for situation recognition

机译：大型地理标记照片的半监督学习以识别情况

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摘要

Photos are becoming spontaneous, objective, and universal sources of information. This paper explores evolving situation recognition using photo streams coming from disparate sources combined with the advances of deep learning. Using visual concepts in photos together with space and time information, we formulate the situation detection into a semi-supervised learning framework and propose new graph-based models to solve the problem. To extend the method for unknown situations, we introduce a soft label method that enables the traditional semi-supervised learning framework to accurately predict predefined labels as well as effectively form new clusters. To overcome the noisy data which degrades graph quality, leading to poor recognition results, we take advantage of two kinds of noise-robust norms which can eliminate the adverse effects of outliers in visual concepts and improve the accuracy of situation recognition. Finally, we demonstrate the idea and the effectiveness of the proposed models on Yahoo Flickr Creative Commons 100 Million. (C) 2017 Elsevier Inc. All rights reserved.
机译：照片正在成为自发，客观和普遍的信息来源。本文使用来自不同来源的照片流以及深度学习的进展，探索了不断发展的态势识别。利用照片中的视觉概念以及时空信息，我们将情况检测公式化为半监督学习框架，并提出了基于图的新模型来解决该问题。为了将方法扩展到未知情况，我们引入了一种软标签方法，该方法使传统的半监督学习框架能够准确预测预定义标签，并有效地形成新的集群。为了克服会降低图形质量，导致识别效果不佳的嘈杂数据，我们利用了两种鲁棒性准则，可以消除视觉概念中异常值的不利影响，并提高态势识别的准确性。最后，我们在Yahoo Flickr Creative Commons 1亿上证明了所提出模型的想法和有效性。 （C）2017 Elsevier Inc.保留所有权利。

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