METHODS OF RELEVANT IMAGES SEARCH BASED ON THE MEDIAN PROCESSING OF STRUCTURAL DESCRIPTIONS

Authors

  • V. A. Gorokhovatsky Kharkiv Educational and Scientific Institute SHEI «The University of Banking», Kharkiv, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2017-1-12

Keywords:

computer vision, structural image recognition, set of characteristic features, descriptors, SURF, median processing, relevant definitions, recognition performance.

Abstract

Context. Intensive development and expansion of the application possibilities of modern computer vision systems requires in-depth
research and creation of more efficient and versatile visual information processing methods. The main problems are related with the research
and improvement of information recognition technology in an integrated feature space with regard to the descriptions in the form of image
point set descriptors (SURF-features), as well as the necessity of recognition performance estimation in a practical applications.
Objective. Article is focused on the research of possibility of constructing and evaluating the effectiveness of median processing models
to perform structural recognition of objects in the image in terms of obtaining a compression data in feature space of image database.
Method. Transformation of space structural features into vector space in order to increase the speed of the recognition process was
proposed. Median processing of descriptions to form ordered finite list of descriptors was proposed as transform method. The result is the
creation of method to form and calculate the relevance of image descriptions in the transformed feature space.
Results. Implementation of median characteristics analysis to form vector representation allowed to reduce the amount of computing
costs significantly and improve recognition performance. Recognition time in comparison with the traditional approach is reduced hundreds
of times preserving required efficiency. Simulation and experimental research of the proposed recognition method on the test dataset was performed on the basis of SURF descriptions. Effectiveness in terms of performance is confirmed, comparative evaluation of the quality of recognition for a variety of treatment options is obtained. Conclusions. Perspective properties recognition systems in the space of the structural features of images are systematized. The median
analysis allows to reveal new patterns in initial information to provide effective fast recognition. Scientific novelty of research is the method of structural image recognition by applying the median analysis to form compressed vector representation of set of descriptors in the structural description of an image. Move to the vector-listed view considerably improves performance by simplifying recognition processing.
Application value of the work is to provide practical programming models for the modification of structural method for detection and
confirmation of the effectiveness of the proposed approach in the specific datasets. 

References

Гороховатский В. А. Структурный анализ и интеллектуальная обработка данных в компьютерном зрении : монография / В. А. Гороховатский. – Х. : Компания СМИТ, 2014. – 316 с. 2. Szeliski R. Computer Vision: Algorithms and Applications / R. Szeliski. – London : Springer, 2010. – 979 p. 3. Quality Criteria for Multidimensional Object Recognition Based Upon Distance Matrices / [A. V. Gorokhovatsky, V. A. Gorokhovatsky, A. N. Vlasenko, N. V. Vlasenko] // Telecommunications and Radio Engineering. – 2014. – Vol. 73, No. 18. – P. 1661–1670. 4. Кохонен Т. Самоорганизующиеся карты / Т. Кохонен ; пер. 3-го англ. изд. – М. : БИНОМ, Лаборатория знаний, 2013. – 655 с. 5. Гороховатский В. А. Систематизация пространства структурных признаков на основе методов самообучения в целях результативного распознавания изображений / В. А. Гороховатский, А. Е. Берестовский, Е. О. Передрий // Радиоэлектроника, информатика, управление. – 2016. – № 2 (37). – C. 87–94. 6. Bay H. Surf: Speeded up robust features / H. Bay, T. Tuytelaars, L.Van Gool // Computer Vision: Ninth European Conference on Computer Vision, Graz, 7–13 May, 2006: proceedings. – Berlin : Springer, 2006. – P. 404–417. 7. Прикладная статистика: Классификация и снижение размерности : справ. изд. / [С. А. Айвазян, В. М. Бухштабер, И. С. Енюков, Л. Д. Мешалкин] ; под ред. С. А. Айвазяна. – М. : Финансы и статистика, 1989. – 607 с. 8. Proceedings of the 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, August 23–27, 2016. – Lviv : Publishing House of Lviv Polytechnic National University, 2016.– 418 p. 9. Осовский С. Нейронные сети для обработки информации / С. Осовский [пер. с польского]. – М. : Финансы и статистика, 2002. – 344 с. 10. Гороховатский В. А. Применение лингвистических представлений структурных описаний в распознавании изображений / В. А. Гороховатский, М. М. Гридин // Інтелектуальні системи прийняття рішень та проблеми обчислювального інтелекту (ISDMCI’2016): матер. міжн. наук. конф., с. Залізний Порт, 24–28 травня 2016. – Херсон : ПП Вишемирський В. С. – С. 271–273.

Downloads

How to Cite

Gorokhovatsky, V. A. (2017). METHODS OF RELEVANT IMAGES SEARCH BASED ON THE MEDIAN PROCESSING OF STRUCTURAL DESCRIPTIONS. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2017-1-12

Issue

No. 1 (2017): Radio Electronics, Computer Science, Control

Section

Progressive information technologies

License

Copyright (c) 2017 V. A. Gorokhovatsky

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons Licensing Notifications in the Copyright Notices

The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.

The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.

The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.