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Authors:
Asaya Shimojo
Shoichi Uratani
Keywords: Grad-CAM, Brand Identity, Visual Identity, Package Design, Consumer Recognition
Abstract:
Many brands traditionally rely on qualitative methods to design their product packaging, leaving uncertainties about the consistency of brand identity across different packages. This study leverages machine learning to quantitatively extract and analyze design elements that resonate with consumers’ perception of brand identity. Specifically, we employed Grad-CAM, an interpretative method for Convolutional Neural Networks (CNNs), to identify crucial visual features—termed Visual Identities—within the middle layers of a model trained on specific brand package images. These features were analyzed to determine their influence on package classification and their alignment with human perception of brand identity. Our findings demonstrate that the machine learning approach approximates human perception closely, providing a novel quantitative method to enhance and maintain brand identity. Additionally, we quantified the contribution of each identified Visual Identity to overall brand recognition, offering a more systematic approach to understanding and preserving a brand’s distinctiveness that has traditionally been handled qualitatively.
Pages: 13 to 18
Copyright: Copyright (c) IARIA, 2024
Publication date: June 30, 2024
Published in: conference
ISBN: 978-1-68558-183-1
Location: Porto, Portugal
Dates: from June 30, 2024 to July 4, 2024