Abstract
Content-based image retrieval (CBIR) has been attracting increased attention recently thanks to its large range of practical applications. In the past, the most common method of search was keyword search with the advantage of simplicity and ease of use; however eventually it is typically time consuming and impractical to capture the sufficient keywords to describe the humans’ requirements. In recent years, along with the development of science and technology, image retrieval has become a favorite subject to be researched. In this paper, a content-based image retrieval system is presented based on the self-built convolutional neural network deep learning model and Cosine similarity technique. The results on CIFAR-10 dataset show that the method is an attractive alternative to existing methodologies thanks to its wide range of possible uses.
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Hung, B.T., Phuong, P.H. (2022). Content Based Image Retrieval Based on Deep Learning Approach. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_25
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DOI: https://doi.org/10.1007/978-981-16-8225-4_25
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