Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification.

Journal: Computational intelligence and neuroscience
Published Date:

Abstract

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.

Authors

  • Jiangfan Feng
    College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
  • Yuanyuan Liu
    College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
  • Lin Wu
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.