Feng Jiangfan, Liu Yuanyuan, Wu Lin
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Comput Intell Neurosci. 2017;2017:5169675. doi: 10.1155/2017/5169675. Epub 2017 Jun 19.
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories.
随着地理标记图像的广泛使用，越来越多的研究工作集中在地理场景分类上。在地理场景分类中，有效的空间特征选择可以显著提高最终性能。视觉词袋（BoVW）在地理场景分类中的特征选择方面表现良好；然而，只有在提供的特征提取器匹配良好的情况下，它才能有效工作。在本文中，我们使用卷积神经网络（CNN）来优化提出的特征提取器，以便它能够从地理标记图像中学习更合适的视觉词汇。作为地理场景分类的工具，我们的方法在包含各种场景类别的三个数据集中分别比BoVW取得了更好的性能。
