【目的】利用用戶對圖像標注的標簽提出用戶標簽框架,并通過用戶標簽框架總結深度學習自動標注圖像的不足。【方法】統計分析從Flickr上下載的大約100萬張圖像數據集中的用戶標簽,抽取高頻詞進行用戶標簽框架匹配。將用戶標簽與Image Net數據庫標簽進行對比總結。對含有高頻詞的圖像使用MXNet深度學習算法進行標注,分析標注結果。【結果】當前深度學習自動標注,在圖像背景知識、總體描述以及人類感官描述等方面還存在缺陷。【局限】數據集的范圍需要擴大,深度學習算法的種類需要增加。【結論】自動標注圖像的發展,需要建立圖像信息與背景知識、描述等的聯系;并且深度學習未來發展還需要賦予計算機邏輯推理以及情境感知的能力。

[Objective] This paper proposes a user tagging framework and examines the limitations of tagging image with deep learning techniques, aiming to improve the performance of automatic annotation services. [Methods] We analyzed the user-added tags from one million images on flickr.com to extract the high frequency ones. Then, we mapped these tags with the proposed framework, and compared them with tags from the Image Net database. Finally, we analyzed images with high frequency tags with the deep learning algorithm-MXNet. [Results] The automatic image annotation techniques based on deep learning could not effectively understand the image’s background knowledge, as well as the image’s descriptions from the human perceptive. [Limitations] Our dataset needs to be expanded and analyzed with other deep learning algorithms. [Conclusions] The development of automatic image annotation, requires us to establish the association between image information, background knowledge, and description, as well as cultivate deductive reasoning and context-aware abilities.