Abstract
To further optimize the existing methods in the field of computer vision and improve the intelligence of image data mining technology, the relevant feedback technology is combined with traditional image data mining technology, and an image data mining technology based on the relevant feedback K-Nearest-Neighbor (K-NN) algorithm is designed and further optimized. The focus is the actual feature extraction test for image color and shape. The test results reveal that for retrieval of K = 1, K = 2, K = 3 images, both positive feedback K-NN algorithm and negative feedback K-NN algorithm can effectively improve the accuracy of image data mining. Among them, negative feedback K-NN algorithm has the highest accuracy for image shape feature extraction. When there are K = 3 images, the accuracy of image data mining can reach 78.3%. Then, the image mining research is conducted on multiple databases. In a total of four databases, the accuracy of image retrieval increases with the increase of feedback times. At the same time, using the optimized KNN algorithm can greatly improve the accuracy of image feature extraction, and the highest accuracy can reach 99.3%. The research content can provide a scientific reference for the follow-up study of KNN algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akhtar N, Mian A (2018) Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6:14410–14430
Beck APA, Araujo Junior E, Leslie ATFS et al (2015) Assessment of fetal lung maturity by ultrasound: objective study using gray-scale histogram. J Matern Fetal Neonatal Med 28(6):617–622
Chakraborty S, Karaa WBA, Dey N et al (2015) Image mining framework and techniques: a review. Ann De Dermatologie Et De Vénéréologie 1:237–244
Dai WZ, Yang Y (2019) Noise image edge detection based on improved Gauss-Laplace operator. Appl Res Comput 36(8):2544–2547
Deshmukh J, Bhosle U (2016) Image mining using association rule for medical image dataset. Procedia Comput Sci 85:117–124
Dong W, Xu ZH, Li XX et al (2020) Low-cost subarrayed sensor array design strategy for IoT and future 6G applications. IEEE Internet of Things J 7(6):4816–4826
Gharbia R, Hassanien AE, El-Baz AH et al (2018) Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications. Future Gener Comput Syst 88:501–511
Gonzalez CI, Melin P, Castro JR et al (2016) An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft Comput 20(2):773–784
Hsu WY, Chou CY (2015) Medical image enhancement using modified color histogram equalization. J Med Biolog Eng 35(5):580–584
Lu H, Setiono R, Liu H (2016) Effective data mining using neural networks. Knowl Data Eng IEEE Trans 8(6):957–961
Neto MM, Filho R, Paulo J et al (2018) Algorithm to detect nitrogen foliar deficiency in bean crops applying digital image processing and data mining. Int J Dev Res 8(10):23547–23552
Nie S, Zheng M, Ji Q (2018) The deep regression bayesian network and its applications: probabilistic deep learning for computer vision. IEEE Signal Process Mag 35(1):101–111
Sharmila A, Geethanjali P (2016) DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4:7716–7727
Tang Y, Wang H, Guo K et al (2018) Relevant feedback based accurate and intelligent retrieval on capturing user intention for personalized websites. IEEE Access 6:24239–24248
Yu YY, Zhang Y, Zhang JS (2016) Research summary of relevant feedback in information retrieval. Inf Stud Theory Appl 39(12):135–139
Zahradnikova B, Duchovicova S, Schreiber P (2015) Image mining: review and new challenges. Int J Adv Comput Sci Appl 6(7):242–246
Zhao YP (2017) Vehicle target recognition method based on image mining technology. Comput Meas Control 25(2):160–163
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ye, Z., Su, L. The use of data mining and artificial intelligence technology in art colors and graph and images of computer vision under 6G internet of things communication. Int J Syst Assur Eng Manag 12, 689–695 (2021). https://doi.org/10.1007/s13198-021-01063-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01063-5