Camouflage performance analysis and evaluation framework based on features fusion

Abstract

The analysis and evaluation of camouflage performance is an important procedure in digital camouflage pattern design, as it helps to improve the design quality of camouflage patterns. In this paper, we propose a novel framework that uses the nonlinear fusion of multiple image features to quantitatively evaluate the degree to which the target and surrounding background differ with respect to background-related and internal features. In our framework, background-related features are first formulated as a measure of conspicuousness, which is calculated and quantized by the saliency detection method, whereas internal features refer to the interior saliency of camouflage textures, such as lines and other regular patterns. These two features are fused to evaluate the camouflage effect. A subjective evaluation is carried out as the baseline of our evaluation model. Experimental results show that our camouflage evaluation framework accords with the human visual perception mechanism, and is an effective method for evaluating camouflage pattern design.