个性化Web图像检索关键技术研究

发布时间：2019-01-25 19:37

【摘要】：目前,信息与计算机技术的迅速发展,Internet中的信息资源急剧增加,包含了—些简单的文本数据,还包括了大量的图像、视频等多媒体信息。因此,如何高效、准确地从海量Web图像资源中检索到用户兴趣信息成为当前的一个研究热点。同时,对于Web图像检索,目前的大多搜索引擎提供的图像检索服务没有考虑到用户需求的差异性,因此,随着Web中图像数量的迅速增加,便会消耗大量的检索时间,降低图像检索的效率。于是人们希望能够及时获得所感兴趣的信息资源,针对不同的需求为自己提供个性化的服务。针对以上问题,并结合Web图像检索的自身特点,本文提出了个性化Web图像检索技术研究。首先,根据目前的信息检索服务没有考虑用户的差异,而导致检索效率比较低的问题,本文提出了一种基于用户兴趣模型的个性化Web图像检索算法。首先给出了用户兴趣模型的形式化定义；同时考虑到随着时间的变化,用户兴趣会发生改变这个问题,本文又引入了新奇因子,有效地结合了短期兴趣和长期兴趣；然后采用显式跟踪和隐式跟踪相结合的方法对用户的兴趣进行学习,以不断完善用户的兴趣信息。通过用户兴趣模型可以根据不同用户的不同兴趣爱好为用户提供个性化的Web图像检索服务,极大地提高了图像检索的效率。个性化Web图像检索中还存在另外一个关键的技术问题,那便是用户兴趣的迁移问题,即具有相似兴趣爱好用户之间的兴趣信息转移问题。目前,这种技术也称为个性化推荐。针对这一问题,本文提出了基于用户兴趣模型的个性化用户兴趣推荐算法,此算法采用了SVD技术和K-means聚类相融合的协同过滤方法,有效克服了评分矩阵数据的稀疏问题,同时有效解决了个性化Web图像检索中用户兴趣转移问题,为新老用户提供了个性化的推荐服务,大大提高了用户对感兴趣信息的检索速度和效率。最后,本文完成了一个支持多模态查询的个性化Web图像检索系统,对全文的工作进行了总结,并给出了未来本课题需要进一步研究和提高的问题。
[Abstract]:At present, with the rapid development of information and computer technology, the information resources in Internet increase rapidly, including some simple text data, and a lot of multimedia information, such as images, video and so on. Therefore, how to efficiently and accurately retrieve user interest information from massive Web image resources has become a hot research topic. At the same time, for Web image retrieval, most of the current search engines provide image retrieval services that do not take into account the difference of user needs. Therefore, with the rapid increase of the number of images in Web, a large amount of retrieval time will be consumed. Reduce the efficiency of image retrieval. So people hope to be able to get interested information resources in time, to provide personalized services for different needs. Aiming at the above problems and combining the characteristics of Web image retrieval, this paper puts forward the research of personalized Web image retrieval technology. Firstly, a personalized Web image retrieval algorithm based on user interest model is proposed in this paper, because the current information retrieval service does not consider the difference of users, which leads to low retrieval efficiency. Firstly, the formal definition of user interest model is given, and considering the problem that user interest will change with time, the novelty factor is introduced in this paper, which effectively combines short-term interest with long-term interest. Then we use the combination of explicit tracking and implicit tracking to study the interest of users in order to improve the interest information of users. The user interest model can provide users with personalized Web image retrieval service according to different users' different interests, which greatly improves the efficiency of image retrieval. Another key technical problem in personalized Web image retrieval is the migration of user interest, that is, the problem of information transfer between users with similar interests. At present, this technology is also called personalized recommendation. In order to solve this problem, this paper proposes a personalized user interest recommendation algorithm based on user interest model. This algorithm uses SVD technology and K-means clustering fusion to effectively overcome the sparse problem of scoring matrix data. At the same time, it effectively solves the problem of user interest transfer in personalized Web image retrieval, provides personalized recommendation services for new and old users, and greatly improves the speed and efficiency of users' retrieval of information of interest. Finally, a personalized Web image retrieval system supporting multi-modal query is completed, and the work of the thesis is summarized, and the problems that need to be further studied and improved in the future are given.
【学位授予单位】：东北林业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP391.3

【参考文献】