Cultural and Creative Product Design and Image Recognition Based on the Convolutional Neural Network Model

Area of Science:

• Artificial Intelligence
• Computer Vision
• Product Design

Background:

• Technological advancements have led to the integration of artificial intelligence (AI) in diverse sectors.
• Cultural and creative products, characterized by the incorporation of cultural symbols and factors, are gaining market prominence.
• Image recognition is crucial for analyzing and categorizing visual data in product design.

Purpose of the Study:

• To investigate the application of artificial intelligence, specifically convolutional neural networks (CNNs), for image recognition in cultural and creative product design.
• To evaluate the effectiveness of a CNN model in identifying and analyzing visual elements of cultural and creative products.

Main Methods:

• The study employed a convolutional neural network (CNN), a type of artificial neural network (ANN) designed for visual image analysis.
• The proposed system utilized the CNN model for the specific task of image recognition within the domain of cultural and creative product design.

Main Results:

• The convolutional neural network model achieved a recognition accuracy of 87% for cultural and creative product designs.
• The results demonstrate the capability of CNNs to effectively analyze and classify visual features pertinent to cultural and creative product aesthetics.

Conclusions:

• Convolutional neural networks are a viable and effective tool for image recognition in the field of cultural and creative product design.
• The successful application of CNNs highlights their potential to support innovation and development in the cultural and creative industries.