ATiTHi: Deep Learning and Hybrid Optimization for Accurate Tourist Destination Classification
Keywords:
Content-based image classification, Tourist destination exploration, Convolutional Neural Networks (CNNs), Transfer learningAbstract
This research introduces an innovative approach to tourist destination exploration through content-based image classification, leveraging convolutional neural networks (CNNs). Recognizing the pivotal role of visual content in understanding tourism preferences and marketing destinations, the study focused on India. A dataset, named Indian Trajectory, was curated, comprising six thousand images categorized into six major tourist destination classes. Transfer learning strategies, utilizing pretrained weights from ImageNet, were employed to address the challenge of limited dataset size. Six prominent CNN models VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50, and AlexNet were initialized with pretrained weights and adapted classifiers for tourist image classification. Hyperparameter optimization, through a hybrid approach, enhanced the efficiency of the proposed Atithi model. Performance comparison indicated that VGG-16 outperformed other models, achieving an accuracy of 98. This result surpassed AlexNet (84.12), MobileNetV2 (96.97), VGG-19 (93.99), InceptionV3 (91.79), and ResNet-50 (87.08). Overall, the study demonstrates the potential of CNNs and transfer learning in automating the analysis of tourist photos for a more satisfying and market-oriented tourism experience.
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