電腦視覺圖像辨識技術，受惠於近幾年來深度學習各類演算法的發展演進，搭配GPU運算能力的支援，在天時地利的情況下有了不同於傳統只能辨識簡單圖形的能力，通過深度學習的強大學習能力，圖像辨識的能力及精確度，已接近甚至超越人類所能，足以協助人類處理圖像辨識的工作。
本研究目的是圖像檢索系統，基於深度學習的方式，使用卷積自編碼神經網路並引用Stanford Dogs Dataset 和UECFOOD256 Dataset等不同類型資料集，先進行自編碼模型的訓練，再利用自編碼模型中的編碼器進行圖像特徵提取，再將特徵數據降維後，通過距離演算法的計算找出特徵近似的圖像，提出一套最可行的圖像檢索系統。

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.

論文審定書 i
摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖次 vii
表次 viii
1 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
2 文獻探討及相關研究 3
2.1 深度學習 3
2.2 卷積神經網路（Convolutional Neural Network） 5
2.2.1 卷積層(Convolutional Layer) 6
2.2.2池化層(Pooling Layer) 7
2.2.3完全連接層(Full connect Layer) 8
2.3 自編碼(Autoencoder) 9
3 圖像檢索系統研究與實作方法 10
3.1 研究方法 10
3.1.1 PCA(Principal Component Analysis) 11
3.1.2 t-SNE (t-distributed Stochastic Neighbor Embedding) 12
3.1.3距離演算法 13
3.1.3.1 歐基里德距離(Euclidean Distance) 13
3.1.3.2 城市街區距離(Cityblock Distance) 14
3.1.3.3夾角餘弦(Cosine Distance) 15
3.1.3.4 相關距離(Correlation Distance) 16
3.1.3.5 BrayCurtis 距離(Braycurtis Distance) 16
3.1.3.6坎培拉距離(Canberra Distance) 17
3.1.3.7切比雪夫距離(Chebyshev Distance) 18
3.2 使用預訓練模型實作 18
3.3 深度卷積自編碼實作 20
4 實驗結果及討論 22
4.1 實驗結果 22
4.2 討論 29
5 參考文獻 31

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