返回信息流当前，随着人们生活水平的不断提高，旅游越来越成为人们日常生活娱乐的首选方式。而近年来互联网产业处在一个高速发展期，正在以惊人的速度改变着我们的生活。互联网与传统产业的结合成为当前新的热点和趋势。将互联网与旅游相结合，为旅游产业注入新的活力越来越引起人们的兴趣。在旅游产业，来自互联网及物联网的海量感知数据包括了多种媒体类型的信息，且这些信息相互间具有一定的相关性，呈现出跨媒体的特征。因此，高效的对这些数据进行组织挖掘和综合利用，对加强旅游产业的认知，提高旅游产业生产效率，完成产业转型，具有重大意义。本选题旨在对跨媒体旅游数据的语义识别方法进行研究。
本论文设计的系统通过设计并实现具有异构、高维和多阶特性的跨媒体旅游数据的语义提取、描述与建模方法，提取具有一致性的跨媒体语义特征，建立跨媒体旅游数据的语义分析模型。该跨媒体旅游数据语义分析模型应既能充分表达跨媒体旅游数据的语义含义，又便于构建海量跨媒体的索引结构。由于旅游数据的多媒体数据大量涌现，单一媒体的语义分析技术已无法满足整个数据集的语义分析需求，存在语义缺失、主题空间不统一，语义融合困难等问题。在这里提出一种跨媒体语义分析方法，首先以概率生成方法分别对文本和图像数据进行语义分析，然后利用跨媒体数据间的语义相关性进行跨媒体语义分析学习，建立跨媒体语义分析模型，进而实现文本数据与图像数据之间的映射。
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At present, with the continuous improvement of people's living standard, tourism become the preferred way of People's Daily life entertainment. But in recent years, the Internet industry in a high-speed development period, is changing our lives at an alarming rate. The combination of the Internet and traditional industries become the new hot spots and trends. Combining Internet and tourism, tourism industry has injected new vitality increasingly aroused people's interest. In the tourism industry, from the Internet and Internet of things the huge amounts of sensory data includes a variety of media types information, and have a certain correlation between the information interaction, shows the characteristics of cross media. Therefore, the efficient organization these data mining and comprehensive utilization, to strengthen the cognition of the tourism industry, improve the efficiency of industry production, complete industry transformation, is of great significance. This paper aimed to travel across the media data semantic identification method were studied.
This paper designs the system through the design and implementation with high heterogeneous, peacekeeping, multistage properties across media travel data semantic extraction, description and modeling method of semantic feature extraction with consistency across media, cross-media travel data semantic analysis model is established. The cross-media tourism data semantic analysis model should be can not only express the cross-media tourism data semantic meaning, and convenient for construction of mass media index structure. Because of the travel data of multimedia data, the semantic analysis of a single media technology has been unable to meet the demand of the whole data set of semantic analysis, there lack of semantic and topic space is not unified, semantic integration difficulties and other issues. A cross-media semantic analysis method is put forward here, first of all, based on probability generating method respectively to semantic analysis of text and image data, and then use across different media semantic correlation between data media semantic analysis study, cross-media semantic analysis model is established, which realizes the text data and the mapping between the image data.
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【 在 wh131 的大作中提到: 】
: At present, with the continuous improvement of people's living standard, tourism become the preferre
: .........
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【 在 wh131 (华1哥) 的大作中提到: 】
: At present, with the continuous improvement of people's living standard, tourism become the preferred way of People's Daily life entertainment. But in recent years, the Internet industry in a high-speed development period, is changing our lives at an alarming rate. The combination of the Internet and traditional industries become the new hot spots and trends. Combining Internet and tourism, tourism industry has injected new vitality increasingly aroused people's interest. In the tourism industry, from the Internet and Internet of things the huge amounts of sensory data includes a variety of media types information, and have a certain correlation between the information interaction, shows the characteristics of cross media. Therefore, the efficient organization these data mining and comprehensive utilization, to strengthen the cognition of the tourism industry, improve the efficiency of industry production, complete industry transformation, is of great significance. This paper aimed to travel across the media data semantic identification method were studied.
: This paper designs the system through the design and implementation with high heterogeneous, peacekeeping, multistage properties across media travel data semantic extraction, description and modeling method of semantic feature extraction with consistency across media, cross-media travel data semantic analysis model is established. The cross-media tourism data semantic analysis model should be can not only express the cross-media tourism data semantic meaning, and convenient for construction of mass media index structure. Because of the travel data of multimedia data, the semantic analysis of a single media technology has been unable to meet the demand of the whole data set of semantic analysis, there lack of semantic and topic space is not unified, semantic integration difficulties and other issues. A cross-media semantic analysis method is put forward here, first of all, based on probability generating method respectively to semantic analysis of text and image data, and then use across different media semantic correlation between data media semantic analysis study, cross-media semantic analysis model is established, which realizes the text data and the mapping between the image data.
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