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Computational Intelligence and Neuroscience
Volume 2017, Article ID 5169675, 14 pages
https://doi.org/10.1155/2017/5169675
Research Article

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

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Correspondence should be addressed to Yuanyuan Liu; nc.ude.tpuqc.uts@830132041s

Received 13 February 2017; Revised 31 March 2017; Accepted 18 May 2017; Published 19 June 2017

Academic Editor: Athanasios Voulodimos

Copyright © 2017 Jiangfan Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.