近年來深度學習技術蓬勃發展，其中圖像辨識的領域已有著卓越的表現及普遍的應用，舉凡目前的臉書，Google，無人商店等等，這些科技早已緊密與我們的生活結合，我們可以發現社群網路常常被分享的都是吃喝玩樂的訊息，尤其是美食圖片的分享，往往讓人心情愉悅忍不住食指大動，但往往我們都忽視這些美食背後隱藏的致命吸引力，由於近年來食安風暴頻傳，所以我們對吃的內容更應注意，我們要多吃食物而不是食品，避免加工過以及高油高鹽的食物，現代人有太多疾病都是攝取過量的不健康的食品所導致。
緣此，本研究設計主要以圖像組成的食材辨識系統，使用深度多標籤學習與深度編碼器的非負矩陣分解（DANMF）法實作食譜食材辨識的技術。本研究引用MIT(麻省理工學院)提供之 Recipe1M 資料集，利用這些圖像資料訓練模型，透過模型可分辨食材料理風格解決大量多標籤的問題，並運用模型局部可解釋器(LIME)，來說明圖片中成分預測部位，增加模型的可解釋性。

In recent years, deep learning technology has flourished, and the field of image recognition has been excellent in performance and universal application. Today's Facebook, Google, unmanned stores, etc. have been closely integrated with our lives. We can find posts of eating and drinking are often shared on social media, especially the sharing of food pictures, which often makes people feel happy and finger licking, but often we ignore the deadly attraction hidden behind these foods. For the past few years, there are frequent incidents of food safety, so we should pay more attention to the composition of food. We should eat more whole foods instead of processed foods, avoid processed and high-oil and high-salt foods. Modern people have too many diseases caused by excessive intake of unhealthy foods.
In this study, the Recipe-Ingredient recognition system consisting mainly of images was used, and the method of deep multi-label learning and deep autoencoder-like non-negative matrix factorization (DANMF) was used to implement the Recipe-Ingredient recognition method. This study uses the Recipe1M dataset provided by Massachusetts Institute of Technology to train models using these image data, and solve a large number of multi-label problems through the model-resolved food material style. Furthermore, Local Interpretable Model-Agnostic Explanations (LIME) was employed to explain the prediction parts of the components in the picture and increase the interpretability of the model.

誌謝 ii
摘要 iii
Abstract iv
第一章 緒論 1
(1-1)研究背景 1
(1-2)研究動機 3
(1-3)研究目的 4
第二章 文獻探討 5
(2-1)深度學習 （Deep Learning） 5
(2-2)卷積神經網路（Convolutional Neural Network） 6
(2-3)多標籤學習(Multi-Label Learning) 8
(2-4)非負矩陣分解 (Non-negative Matrix Factorization) 10
(2-5)Deep Autoencoder-like Non-negative Matrix Factorization (DANMF) 11
(2-6)Local Interpretable Model-Agnostic Explanations (LIME) 12
第三章 研究方法 13
(3-1)原始資料引用 15
(3-2)資料前處理 15
(3-3)分析與建立食材資料集 16
(3-4)運用多標籤學習方法建構深度學習卷積神經網路 19
(3-5)運用圖片辨識模型程序預測料理風格 20
(3-6)料理風格解釋 21
第四章 實驗結果 22
第五章 結論 25
第六章 參考文獻 26

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