Volume 37 Issue 1
Jan.  2015
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NIU Jie, BU Xiongzhu, QIAN Kun, LI Zhong. An Indoor Scene Recognition Method Combining Global and Saliency Region Features[J]. ROBOT, 2015, 37(1): 122-128. DOI: 10.13973/j.cnki.robot.2015.122
Citation: NIU Jie, BU Xiongzhu, QIAN Kun, LI Zhong. An Indoor Scene Recognition Method Combining Global and Saliency Region Features[J]. ROBOT, 2015, 37(1): 122-128. DOI: 10.13973/j.cnki.robot.2015.122
shu

An Indoor Scene Recognition Method Combining Global and Saliency Region Features

  • 1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;

  • 2. School of Electrical and Electronic Engineering, Changzhou College of Information Technology, Changzhou 213164, China;

  • 3. School of Automation, Southeast University, Nanjing 210096, China

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  • Received Date: March 16, 2014
  • Revised Date: July 10, 2014
  • Available Online: October 26, 2022
  • Published Date: January 19, 2015
    Graphical Abstract
    • Abstract
  • Conventional scene recognition methods have poor performance in indoor situations. For this reason, an indoor scene recognition method for mobile robots is presented, combining global and saliency region features. In addition to the use of an improved BoW (Bag-of-Words) model for indoor scene recognition, an improved BDBN (bilinear deep belief network) model is implemented, using information from a salient region detection technique. The first and the second winners of the salient region detection with the visual attention approach are sent into the improved BDBN model to automatically learn image features and to judge the class sets they belong to. The final result of the indoor scene recognition can be obtained by combining the above-mentioned two models through strategies for a piecewise discriminant. The method is applied to the real mobile robot platform and the standard MIT 67-category indoor scene dataset. The experiments show that the proposed method is highly effective, and can improve the accuracy of common BoW-based methods by up to 10%. In addition, the accuracy rate of the method can reach 44.3% in the MIT dataset, which is superior to some methods in the literature.
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    • References (31)
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