Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering
The comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy,...
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doaj-2e6fad22402d4a56a91d039ddb6371772021-03-30T04:37:21ZengIEEEIEEE Access2169-35362020-01-01813430313431010.1109/ACCESS.2020.30114549146652Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and ClusteringWen Wang0https://orcid.org/0000-0003-2950-693XGuoyong Han1Guanglei Sun2School of Management Engineering, Shandong Jianzhu University, Jinan, ChinaSchool of Management Engineering, Shandong Jianzhu University, Jinan, ChinaJinan Institute of Product Quality Inspection, Jinan, ChinaThe comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy, which produces unreliable results. To best address these disadvantages, an automatic comparison method for fractal anti-counterfeit labels is proposed. The method can effectively extract the color features, texture features, and shape features of anti-counterfeit labels, and perform cluster analysis and comparison. First, a color volume histogram is used to extract the color and pixel space information features from the fractal anti-counterfeit labels. To compensate for the deficiency of using a single feature, texture and shape features were also extracted based on the median robust extended local binary patterns (MRELBP) and Hu moments. Next, based on feature extraction, k-means clustering is performed to ensure that as many of the same types of labels as possible can be divided into the same clusters and that the comparison can only be performed on one or a few clusters. The experimental results show that the speed and quality of fractal anti-counterfeit label comparison effectively improved after the clustering analysis. Furthermore, based on visual identification and unreliable comparison results, the proposed method is expected to help consumers quickly identify low-quality, counterfeit products.https://ieeexplore.ieee.org/document/9146652/Clustering methodcolor featuresimilarityshape featurestexture feature |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wen Wang Guoyong Han Guanglei Sun |
spellingShingle |
Wen Wang Guoyong Han Guanglei Sun Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering IEEE Access Clustering method color feature similarity shape features texture feature |
author_facet |
Wen Wang Guoyong Han Guanglei Sun |
author_sort |
Wen Wang |
title |
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering |
title_short |
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering |
title_full |
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering |
title_fullStr |
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering |
title_full_unstemmed |
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering |
title_sort |
fractal anti-counterfeit label comparison using combined image features and clustering |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy, which produces unreliable results. To best address these disadvantages, an automatic comparison method for fractal anti-counterfeit labels is proposed. The method can effectively extract the color features, texture features, and shape features of anti-counterfeit labels, and perform cluster analysis and comparison. First, a color volume histogram is used to extract the color and pixel space information features from the fractal anti-counterfeit labels. To compensate for the deficiency of using a single feature, texture and shape features were also extracted based on the median robust extended local binary patterns (MRELBP) and Hu moments. Next, based on feature extraction, k-means clustering is performed to ensure that as many of the same types of labels as possible can be divided into the same clusters and that the comparison can only be performed on one or a few clusters. The experimental results show that the speed and quality of fractal anti-counterfeit label comparison effectively improved after the clustering analysis. Furthermore, based on visual identification and unreliable comparison results, the proposed method is expected to help consumers quickly identify low-quality, counterfeit products. |
topic |
Clustering method color feature similarity shape features texture feature |
url |
https://ieeexplore.ieee.org/document/9146652/ |
work_keys_str_mv |
AT wenwang fractalanticounterfeitlabelcomparisonusingcombinedimagefeaturesandclustering AT guoyonghan fractalanticounterfeitlabelcomparisonusingcombinedimagefeaturesandclustering AT guangleisun fractalanticounterfeitlabelcomparisonusingcombinedimagefeaturesandclustering |
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