Optimizing Feature Extraction for Website Visuals: A Comparative Study of AlexNet and Inception V3

This study investigates the feature extraction capabilities of two prominent convolutional neural network (CNN) architectures, Inception V3 and AlexNet, in the context of website visuals. With the growing need for automated visual data analysis on the web, effective feature extraction is critical for tasks such as image classification, object detection, and visual content analysis. Inception V3 and AlexNet were selected due to their distinct architectural approaches and widespread use in various computer vision applications. The research employed a controlled experimental design, utilizing a dataset of website visuals collected from platforms like Themeforest. Data preprocessing included image resizing, normalization, and augmentation techniques to enhance model robustness. Both models were fine-tuned on the specific dataset to adapt their pretrained weights from ImageNet to the unique characteristics of website visuals. Performance metrics were used to evaluate the models' effectiveness, including precision, recall, F1 score, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Results indicated that AlexNet outperformed Inception V3 in precision (0.7062 vs. 0.6860) and recall (0.5914 vs. 0.0506), highlighting its efficiency and suitability for more straightforward feature extraction tasks with lower computational costs. In contrast, Inception V3 exhibited superior potential in capturing complex visual patterns but struggled with computational efficiency, as evidenced by its significantly longer inference time (222.9552 seconds compared to 63.2978 seconds for AlexNet). The findings underscore the importance of model selection based on specific application requirements, such as prioritizing speed or depth of feature extraction. Recommendations for practitioners include leveraging AlexNet for resource-constrained environments and opting for Inception V3 when handling complex, multi-scale visual data. Future research could explore additional architectures and optimize models to enhance performance across diverse visual tasks.