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Journal of Robotics
Volume 2019, Article ID 8591035, 12 pages
https://doi.org/10.1155/2019/8591035
Research Article

An Indoor Scene Classification Method for Service Robot Based on CNN Feature

School of Control Science and Engineering, Shandong University, Jinan, 250061, China

Correspondence should be addressed to Guohui Tian; nc.ude.uds@nait.h.g

Received 5 November 2018; Revised 4 April 2019; Accepted 15 April 2019; Published 24 April 2019

Academic Editor: Keigo Watanabe

Copyright © 2019 Shaopeng Liu and Guohui Tian. 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

Indoor scene classification plays a vital part in environment cognition of service robot. With the development of deep learning, fine-tuning CNN (Convolutional Neural Network) on target datasets has become a popular way to solve classification problems. However, this method cannot obtain satisfying indoor scene classification results because of overfitting when scene training datasets are insufficient. To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. The novel feature matching algorithm can further improve the speed of scene classification. In addition, overfitting is eliminated by our method even though the training data is limited. The presented method was evaluated on two benchmark scene datasets, Scene 15 dataset and MIT 67 dataset, acquiring 96.49% and 81.69% accuracy, respectively. The experiment results showed that our method was superior to other scene classification methods in terms of accuracy, speed, and robustness. To further evaluate our method, test experiments on unknown scene images from SUN 397 dataset had been done, and the models based on different training datasets obtained 94.34% and 79.80% test accuracy severally, which proved that the proposed method owned good performance in indoor scene classification.