Open Access
ARTICLE
Classification for Glass Bottles Based on Improved Selective Search Algorithm
Shuqiang Guo1, *, Baohai Yue1, Manyang Gao2, Xinxin Zhou1, Bo Wang3
1 School of Computer Science, Northeast Electric Power University, Jilin, 132000, China.
2 Information and Communication Company, State Grid Tianjin Electric Power Company, Tianjin, 300000, China.
3 Graduate School of Science and Engineering, Iwate University, Morioka, 020-8550, Japan.
* Corresponding Author: Shuqiang Guo. Email: .
Computers, Materials & Continua 2020, 64(1), 233-251. https://doi.org/10.32604/cmc.2020.010039
Received 06 February 2020; Accepted 08 March 2020; Issue published 20 May 2020
Abstract
The recycling of glass bottles can reduce the consumption of resources and
contribute to environmental protection. At present, the classification of recycled glass
bottles is difficult due to the many differences in specifications and models. This paper
proposes a classification algorithm for glass bottles that is divided into two stages,
namely the extraction of candidate regions and the classification of classifiers. In the
candidate region extraction stage, aiming at the problem of the large time overhead
caused by the use of the SIFT (scale-invariant feature transform) descriptor in SS
(selective search), an improved feature of HLSN (Haar-like based on SPP-Net) is
proposed. An integral graph is introduced to accelerate the process of forming an HBSN
vector, which overcomes the problem of repeated texture feature calculation in
overlapping regions by SS. In the classification stage, the improved SS algorithm is used
to extract target regions. The target regions are merged using a non-maximum
suppression algorithm according to the classification scores of the respective regions, and
the merged regions are classified using the trained classifier. Experiments demonstrate
that, compared with the original SS, the improved SS algorithm increases the calculation
speed by 13.8%, and its classification accuracy is 89.4%. Additionally, the classification
algorithm for glass bottles has a certain resistance to noise.
Keywords
Cite This Article
S. Guo, B. Yue, M. Gao, X. Zhou and B. Wang, "Classification for glass bottles based on improved selective search algorithm,"
Computers, Materials & Continua, vol. 64, no.1, pp. 233–251, 2020. https://doi.org/10.32604/cmc.2020.010039