Automatic product region extraction based on colour similarity and saliency detection models
In this paper, product region extraction, which can classify the pixels of the product images as product and background regions, is proposed. The proposed method is based on the handcrafted algorithm using both the colour similarity and the saliency detection. Our experiment, which employed 180 prod...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-06-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2022.2061249 |
| _version_ | 1797661032385085440 |
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| author | Takuya Futagami Noboru Hayasaka |
| author_facet | Takuya Futagami Noboru Hayasaka |
| author_sort | Takuya Futagami |
| collection | DOAJ |
| description | In this paper, product region extraction, which can classify the pixels of the product images as product and background regions, is proposed. The proposed method is based on the handcrafted algorithm using both the colour similarity and the saliency detection. Our experiment, which employed 180 product images, clarified that the proposed method increased all the metric for the extraction accuracy compared with conventional methods based on the handcrafted algorithm. The F-measure, which is the comprehensive metric, was significantly increased by 2.20% or more. Our discussion also found that the proposed method also overcame the shortcoming of the conventional method, because the F-measure for the dataset, the accuracy of which was decreased by the conventional method, was significantly improved. In addition, the F-measure was increased by 0.92% or more for each product category. Further comparison and discussion are included in this paper to provide more focused findings. |
| first_indexed | 2024-03-11T18:39:30Z |
| format | Article |
| id | doaj.art-166298ca146c4d5396f3b0b698191aba |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:30Z |
| publishDate | 2022-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-166298ca146c4d5396f3b0b698191aba2023-10-12T13:43:52ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702022-06-01152132110.1080/18824889.2022.20612492061249Automatic product region extraction based on colour similarity and saliency detection modelsTakuya Futagami0Noboru Hayasaka1Osaka Electro-Communication UniversityOsaka Electro-Communication UniversityIn this paper, product region extraction, which can classify the pixels of the product images as product and background regions, is proposed. The proposed method is based on the handcrafted algorithm using both the colour similarity and the saliency detection. Our experiment, which employed 180 product images, clarified that the proposed method increased all the metric for the extraction accuracy compared with conventional methods based on the handcrafted algorithm. The F-measure, which is the comprehensive metric, was significantly increased by 2.20% or more. Our discussion also found that the proposed method also overcame the shortcoming of the conventional method, because the F-measure for the dataset, the accuracy of which was decreased by the conventional method, was significantly improved. In addition, the F-measure was increased by 0.92% or more for each product category. Further comparison and discussion are included in this paper to provide more focused findings.http://dx.doi.org/10.1080/18824889.2022.2061249product region extractiononline marketsegmentationsaliency detectioncolour similarity |
| spellingShingle | Takuya Futagami Noboru Hayasaka Automatic product region extraction based on colour similarity and saliency detection models SICE Journal of Control, Measurement, and System Integration product region extraction online market segmentation saliency detection colour similarity |
| title | Automatic product region extraction based on colour similarity and saliency detection models |
| title_full | Automatic product region extraction based on colour similarity and saliency detection models |
| title_fullStr | Automatic product region extraction based on colour similarity and saliency detection models |
| title_full_unstemmed | Automatic product region extraction based on colour similarity and saliency detection models |
| title_short | Automatic product region extraction based on colour similarity and saliency detection models |
| title_sort | automatic product region extraction based on colour similarity and saliency detection models |
| topic | product region extraction online market segmentation saliency detection colour similarity |
| url | http://dx.doi.org/10.1080/18824889.2022.2061249 |
| work_keys_str_mv | AT takuyafutagami automaticproductregionextractionbasedoncoloursimilarityandsaliencydetectionmodels AT noboruhayasaka automaticproductregionextractionbasedoncoloursimilarityandsaliencydetectionmodels |