發(fā)布時間：2018-04-20 11:09

  本文選題：跨媒體 + 語義建模�。� 參考：《北京郵電大學》2016年碩士論文

【摘要】：目前,互聯網的飛速發(fā)展給智慧旅游帶來了機遇和挑戰(zhàn),機遇是人們在旅游過程中生產了豐富的跨媒體數據,挑戰(zhàn)是不同模態(tài)跨媒體數據之間的“語義鴻溝”為智慧旅游的發(fā)展造成了很大的障礙。本文針對三個問題進行了研究:跨媒體旅游大數據的語義分析和建模、基于旅游領域本體知識庫推理的圖像語義內容自動標注以及融合GIST特征和人群微觀行為特征的擁擠旅游場景內容識別。論文完成的主要工作如下:(1)提出了一種基于PLSA主題模型的對稱的建模方法,建立了不同模態(tài)跨媒體數據之間的潛在語義關聯模型,克服了傳統(tǒng)方法只能表現不同模態(tài)數據之間顯式關系的缺點。通過本文的建模方法,建立了一個包含文本詞和視覺詞之間映射關系的數據模型。(2)提出了一種基于旅游領域本體知識庫推理機制的圖像語義自動標注算法。在已經建立的跨媒體數據語義模型的基礎上,采用融合主題模型的圖像標注算法對圖像內容進行初步標注。同時,結合旅游領域本體知識庫進行進一步的推理,從而更精確地識別圖像中的內容,得到了更加具體的描述圖像內容的詞,提高了標注效果。通過旅游領域本體知識庫的使用,使標注的結果與具體的景點關聯。與傳統(tǒng)的非對稱算法相比,融合語義主題的對稱圖像標注算法的圖像標注正確率得到了提高。(3)提出了一種融合GIST特征和微觀行為特征的擁擠場景識別方法。對待識別的視頻進行鏡頭分割,對每一個鏡頭進行背景提取,采用基十GIST特征的場景識別算法對場景進行初步判斷,得到初步的識別結果后,根據人群移動的規(guī)律提取人群的微觀行為特征,根據不同場景中人群移動規(guī)律的差異,進行再次場景判斷,從而進一步提高場景識別的精確程度。該方法實現了針對擁擠旅游場景的有效的場景識別。與傳統(tǒng)的基于GIST特征的場景識別算法相比,融入人群的微觀行為特征以后,實驗結果的準確率和召回率得到了進一步提高。(4)設計和實現了跨媒體旅游大數據語義學習和內容識別系統(tǒng),包括跨媒體旅游大數據的語義分析和建模、基于旅游領域本體知識庫推理的圖像標注、融合GIST特征和微觀行為特征的擁擠場景識別三個模塊。通過該系統(tǒng)對上述每一部分的實驗結果進行了驗證。實驗表明上述算法在旅游跨媒體數據語義學習和內容識別方面具有較好的效果。
[Abstract]:At present, the rapid development of the Internet has brought opportunities and challenges to intelligent tourism. The opportunity is that people produce rich cross-media data in the process of tourism. The challenge is that the semantic gap between different modes and media data causes a great obstacle to the development of intelligent tourism. This paper focuses on three problems: the semantic analysis and modeling of cross-media tourism big data. Image semantic content automatic tagging based on tourism domain ontology knowledge base reasoning and content recognition of crowded tourism scene based on GIST feature and crowd micro behavior feature. The main work of this paper is as follows: (1) A symmetric modeling method based on PLSA topic model is proposed, and the latent semantic association model between different modes of cross-media data is established. It overcomes the shortcoming that the traditional method can only express the explicit relationship between different modal data. Through the modeling method in this paper, a data model containing the mapping relationship between text words and visual words is established. (2) an image semantic automatic annotation algorithm based on the reasoning mechanism of ontology knowledge base in tourism domain is proposed. Based on the established cross-media data semantic model, the image tagging algorithm based on the fusion topic model is used to label the image content. At the same time, combined with the tourism domain ontology knowledge base to further reasoning, thus more accurate recognition of the image content, more specific words to describe the image content, improve the tagging effect. Through the use of ontology knowledge base in tourism field, the results of labeling are associated with specific scenic spots. Compared with the traditional asymmetric algorithm, the accuracy of symmetric image tagging algorithm based on semantic topic is improved. (3) A congestion scene recognition method based on GIST feature and micro behavior feature is proposed. The scene recognition algorithm based on the base ten GIST feature is used to judge the scene, and the initial recognition result is obtained after the scene is segmented by shot segmentation, each shot is extracted from each shot, and the scene recognition algorithm based on the base 10 GIST feature is used to judge the scene. According to the law of crowd movement, the micro-behavior characteristics of the crowd are extracted, and the accuracy of scene recognition is further improved by judging the scene again according to the difference of the law of crowd movement in different scenes. This method realizes the effective scene recognition for the crowded tourist scene. Compared with the traditional scene recognition algorithm based on GIST feature, after the microcosmic behavior of the crowd is merged, The accuracy and recall rate of the experimental results are further improved. (4) A cross-media tourism big data semantic learning and content recognition system is designed and implemented, including semantic analysis and modeling of cross-media tourism big data. There are three modules: image annotation based on ontology knowledge base reasoning in tourism domain, and congestion scene recognition based on GIST feature and micro behavior feature. The experimental results of each part are verified by the system. Experiments show that the algorithm is effective in semantic learning and content recognition of travel data across media.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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