Visual Attention Analysis and Optimization Algorithm in Packaging Design

Abstract

Visual attention analysis and optimization algorithms in packaging design refer to computational methods aimed at understanding and improving the visual appeal and effectiveness of packaging materials. This study proposes a novel approach for enhancing packaging design through the integration of visual attention analysis and optimization algorithms, employing autoencoder and whale optimization techniques. Visual attention analysis and optimization algorithm in packaging design is to enhance the effectiveness of packaging materials by maximizing their ability to capture and retain consumer attention. This includes identifying key visual elements within packaging designs that are most likely to attract consumers, optimizing the layout and composition of these elements to increase visual impact, and ultimately improving the overall consumer experience and brand perception. Autoencoders are utilized to capture intricate visual features within packaging designs, aiming to create more creative and eye-catching packaging design, while the whale optimization algorithm facilitates the optimization process. Experimental results demonstrate the superior performance of the developed algorithm across various metrics such as accuracy, precision, recall, and f-measure, compared to existing techniques like RNN, CNN, ANN and SVM. The algorithm's scalability and adaptability are further highlighted through consistent performance across increasing data volumes. Overall, this approach holds promise for revolutionizing packaging design by effectively capturing and retaining consumer attention, thereby enhancing brand perception and product satisfaction.