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  3. Volume 18 (Part 2), 2025
  4. Numerical Optimization Method for Clustering in Content-Based Image Retrieval Systems

Numerical Optimization Method for Clustering in Content-Based Image Retrieval Systems

SISN.
2025;
Volume 18
: pp. 30 - 46
Authors:
  1. Stanislav Danylenko
  2. Serhii Smelyakov
1
Kharkiv National University of Radio Electronics, Software Engineering Department, Ukraine
2
Kharkiv National University of Radio Electronics, Software Engineering Department, Ukraine

The object of the study is the process of organizing a descriptor repository in content-based image retrieval systems. The subject of the study is a method of numerical optimization of descriptor clustering in a multidimensional space. The aim of this work is to develop a clustering optimization method in the Multidimensional Cube model to improve search efficiency. The core idea is to ensure a more uniform distribution of descriptors across clusters by adjusting interval boundaries in each dimension, which reduces imbalance in cluster density and improves retrieval performance. The research methodology combines analytical determination of the number of clusters with numerical optimization of descriptor distribution across intervals. The proposed method, named the Dimension Intervals Numeric Optimization Algorithm, has been implemented in two variants: one for deployment in an external relational database and another for deployment in main memory. Theoretical analysis of computational complexity demonstrated that the proposed approach does not require multiple iterations, unlike the competing methods considered for comparison, namely k-means and the Inverted Multi-Index, and it has lower asymptotic complexity. Experimental evaluation was carried out on a dataset of image descriptors. The results showed that k-means provides the highest clustering quality in terms of cluster density, but requires significantly more time. The proposed method in the main-memory variant demonstrated the best balance between clustering quality and execution time, approaching the quality of the Inverted Multi- Index while outperforming it in runtime. The external-database variant proved slower due to query-processing overheads but is appropriate for scalable systems with centralized data repositories. The conclusion is that applying the developed numerical optimization method enables a more uniform distribution of descriptors across clusters and reduces imbalance in their density.

пошук зображень на основі вмісту
clustering
Multidimensional Cube
optimization
image descriptors
search efficiency
data storage
algorithm complexity
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