| Summary: | 碩士 === 國立中山大學 === 資訊管理學系研究所 === 107 === Computer vision image recognition technology has benefited from the development of various algorithms in deep learning in recent years. With the support of GPU computing power, through the powerful learning ability of deep learning, the ability and accuracy of image recognition is close to or beyond human ability, enough to assist humans in the work of image recognition.
The purpose of this study is an image retrieval system, based on deep learning, using a convolutional autoencoder neural network and citing different types of data sets such as Stanford Dogs Dataset and UECFOOD256 Dataset. First training the autoencoder model, and then using the encoder extract the image features. After reducing the dimensionality of features data, the image of the feature approximation is found by the distance computation.
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