Abstract
Multimedia plays a significant role in our day-to-day life. Large image databases easily exist due to the spread of social websites, cloud storage, and smartphones. Searching by text is normal and easy, but searching by image’s content is more intelligent and sensitive. Therefore, this study develops a content-based image retrieval algorithm that achieved high image retrieval accuracy. Intelligent systems can help in this way and work effectively. This study investigates the three proposed models using deep learning algorithms. These models were applied on three databases of different sizes: Corel1K, Corel8K, Aloi74K, and Cifar-100. The first method uses a convolution neural network (CNN) for feature extraction and image classification. The second method uses CNN and long short-term memory (LSTM), and the last one employs CNN and gated recurrent unit (GRU). The three proposed algorithms have significantly reduced the time complexity. Additionally, they have achieved high image retrieval accuracy compared to state-of-the-art models. The proposed CNN model achieved 96%, 89%, and 98%, for Corel1K, Corel8K, and Aloi74K respectively. The CNN + LSTM model achieved CBIR accuracies of 96.5%, 90.5%, and 98.5% for Corel1k, Corel8k, and Aloi74k respectively. Finally, the CNN + GRU model achieved CBIR accuracies 97%, 91.5%, and 99% for Corel1k, Corel8k, and Aloi74k respectively.
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Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F. (2023). Content-Based Image Retrieval Using Fused Convolutional Neural Networks. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_24
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