A color constancy based flower classification method in the blockchain data lake
Multimedia Tools and Applications, ISSN: 1573-7721, Vol: 83, Issue: 10, Page: 28657-28673
2024
- 6Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures6
- Readers6
Article Description
The efficient classification of flower images will directly affect the accuracy of their automatic recognition. Due to the complexity of the background of flowers, not only the color, shape and texture of flowers are different, but also the illumination factors show significant effect on classification results of flower images during the process of acquiring flower images. Therefore, it is of great practical significance to identify flowers with the help of flower salient features and eliminate lighting factors. In order to reduce the influence of illumination factor on the classification accuracy of flower images and ensure the true transparency of flower images in the process of Internet data transmission, in this paper, we propose a color constancy based flower classification method in the Blockchain Data Lake, short for CCAN, firstly, we design a Blockchain Data Lake framework to ensure the accuracy and originality of the original image data; and then, color constancy mechanism is used to encode the color feature of images, in order to reduce the illumination effects. Thirdly, a convolutional neural network based classifier is proposed to achieve flower classification. Finally, we simulate the performance of CCAN on three different data set in the blockchain Data Lake environment, extensive results show that the proposed CCAN effectively improves the accuracy of flower image classification by minimizing the interference of illumination factors on flower targets.
Bibliographic Details
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know