МЕТОД КЛАСТЕРИЗАЦІЇ ОБ’ЄКТІВ НА ЗОБРАЖЕННІ НА ОСНОВІ ВИБОРУ ОЗНАК
OBJECT CLUSTERIZATION METHOD IN PICTURES BASED ON FEATURE SELECTION
Сторінки: 260-264. Номер: №3, 2022 (309) 
Автори:
ШАМУРАТОВ О. Ю.
Національний університет “Львівська Політехніка”
https://orcid.org/0000-0003-1913-5362
e-mail: oleksii.y.shamuratov@lpnu.ua
Oleksiy SHAMURATOV
Lviv Polytechnic National University
DOI: https://www.doi.org/10.31891/2307-5732-2022-309-3-260-264
Анотація мовою оригіналу
У статті описується розробка методу, що дозволяє створювати кластери на основі вибору ознак. У сучасному світі індустрія розваг в Інтернеті швидко розвивається, створюючи попит на більш якісні продукти. Це в свою чергу призвело до використання штучного інтелекту не тільки в науці, а й у розвагах. На даний момент набирають популярності програми, що дозволяють створювати анімацію обʼєктів на фотографіях. У цій статті представлений підхід до вирішення проблеми визначення об’єктів для анімації
Ключові слова: кластеризація, вибір ознак, анімація
Розширена анотація англійською мовою
The article describes the development of a method that allows you to create clusters based on selecting feature features. In todayʼs world, the entertainment industry on the Internet is developing rapidly, creating a demand for better products. This factor has led to the use of artificial intelligence not only in science but also in entertainment. Currently, applications that allow you to create animations of objects in photos are gaining popularity. This article presents an approach to solving the problem of defining objects for animation. To classify and further identify objects, their characteristics should be determined. This is one of the options for abstraction, in which the input set of properties of the object is reduced to the minimum required number of features by which you can identify the object.
The algorithm can be used to determine the main features of objects, such as area and perimeter, radii of inscribed and circumscribed circles, sides of the described rectangle, number and relative position of angles, gradient of the object histogram. Based on these features clustering and classification of the image are implemented. The artificial neural network was trained on image samples, each class contained from 2528 to 16185 images of 64×64 pixels. 1000 images of objects of each class were then selected for testing. The success of recognition based on a convolutional neural network was evaluated. According to the results, we can conclude that the smaller the invariance of the class, the greater the accuracy of recognition. The amount of data in the training sample has little effect on the accuracy of the algorithm. After calculating the intensity gradient, you should divide the image into a cell and build a histogram of the gradient object for each pocket of cell; the histogram module corresponds to the intensity gradient at the point.
Keywords: clustering, feature selection, animation
Література
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References
- Tien D. B., Ching Y. S., Zi-Cai L., Yuan Y. T., “Computer Transformation of Digital Images and Patterns”, p. 276, 1989.
- Y.-Q. Wang, “An Analysis of Viola-Jones Face Detection Algorithm”, IPOL Journal, 2013.
- Khan H. Abdullah, M. Shamian Bin Zainal, «Efficient eyes and mouth detection algorithm using combination of viola jones and skin color pixel detection» International Journal of Engineering and Applied Sciences, № Vol. 3 № 4, 2013.
- V. Gaede и O. Gunther, “Multidimensional Access Methods”, ACM Computing Surveys, pp. 170-231, 1998.
- S. Khan, H. Rahmani, Syed Afaq Ali Shah, M. Bennamoun, G. Medioni, S. Medioni, “A Guide to Convolutional Neural Networks for Computer Vision”, Morgan & Claypool, p. 207, 2018.
- Sibt ul Hussain, “Machine Learning Methods for Visual Object Detection”. p. 160, 2012.
- P. Arabie, L. J. Hubert, G. De Soete, “Clustering and Classification”, p. 500, 1996.
- D. Parks, “Object Detection and Analysis: A Coherency Filtering Approach”, p. 172, 2008.
- Yongqiang Z., Chen Y., Seong G. K., Quan P., Yongmei C., “Multi-band Polarization Imaging and Applications” 1st ed., p. 204, 2016.
- Manikandan S., “Vision Based Assistive System for Label and Object Detection”, p. 64, 2015.
- Salma H., “Object Detection Using Histogram Of Gradients”, p. 52, 2018.
- Wu J., “Advances in K-means Clustering: A Data Mining Thinking”, Springer Science & Business Media, p. 180, 2021.
- J.Loy, “Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects”, Packt Publishing, p. 308, 2019.
- Brannon W. C., “Object Detection in Low-spatial-resolution Aerial Imagery Using Convolutional Neural Networks”, p. 79, 2019.
- Dataset https://knowyourdata-tfds.withgoogle.com
