Abstract
Social event detection in a static image is a very challenging problem and it’s very useful for internet of things applications including automatic photo organization, ads recommender system, or image captioning. Several publications show that variety of objects, scene, and people can be very ambiguous for the system to decide the event that occurs in the image. We proposed the spatial pyramid configuration of convolutional neural network (CNN) classifier for social event detection in a static image. By applying the spatial pyramid configuration to the CNN classifier, the detail that occurs in the image can observe more accurately by the classifier. USED dataset provided by Ahmad et al. is used to evaluate our proposed method, which consists of two different image sets, EiMM, and SED dataset. As a result, the average accuracy of our system outperforms the baseline method by 15% and 2% respectively.
Abstract (translated by Google)
静态图像中的社交事件检测是一个非常具有挑战性的问题，对于物联网应用（包括自动照片组织，广告推荐系统或图像字幕）非常有用。一些出版物显示，各种各样的对象，场景和人物可能对系统决定图像中发生的事件非常模糊。我们提出了静态图像中用于社交事件检测的卷积神经网络（CNN）分类器的空间金字塔配置。通过将空间金字塔配置应用于CNN分类器，图像中出现的细节可以被分类器更精确地观察到。 Ahmad等人提供的数据集被用来评估我们提出的方法，它由两个不同的图像集，EiMM和SED数据集组成。因此，我们系统的平均准确度分别比基准方法高出15％和2％。
URL
https://arxiv.org/abs/1612.04062