Multi-feature similarity-based evaluation of camouflage effectiveness
K. Karthiga1 and A. Asuntha1
Corresponding Author : K. Karthiga
Recieved : 15-April-2024; Revised : 25-March-2025; Accepted : 26-March-2025
Abstract
Camouflage plays a crucial role in countering reconnaissance and concealing military objects. In the defense industry, camouflage techniques, such as camouflage nets and patterns, aim to minimize detectability by reducing distinctive features. However, existing evaluation methodologies face challenges, including inconsistent visual perception and incomplete evaluation indices. This study proposes a comprehensive similarity-based approach for assessing camouflage effectiveness using both objective and subjective evaluation methods. The objective evaluation quantifies camouflage similarity by analyzing four key indices: image color, brightness, texture, and structure. These are represented as the color similarity index (SC), luminance similarity index (SL), structure similarity index (SS), and texture similarity index (ST). The entropy weighting method (EWM) is employed to optimize feature weighting and extract meaningful information. The subjective evaluation assesses detection time and perceived similarity based on a user study involving 20 participants across four different camouflage scenes. Results indicate that the comprehensive similarity model outperforms conventional evaluation methods, demonstrating superior predictive accuracy. Among the indices, SC plays a dominant role, highlighting the significant impact of color on human visual perception. This study demonstrates that a visual perception-based approach enhances camouflage evaluation accuracy and provides a robust, reliable framework for assessing camouflage performance.
Keywords
Camouflage effectiveness, Similarity index, Objective evaluation, Subjective evaluation, Feature vector, Visual perception.
Cite this article
Karthiga K, Asuntha A. Multi-feature similarity-based evaluation of camouflage effectiveness. International Journal of Advanced Technology and Engineering Exploration. 2025;12(124):414-425. DOI : 10.19101/IJATEE.2024.111100558
