基于用户创建社区的图像搜索改进
Improving Image Search based on User Created Communities
摘要：
基于标签的多媒体内容检索是一个困难的问题，不仅是因为与图像和视频相关联的标签的长度较短，而且还由于由广告商和内容创建者使用的术语不匹配。为了缓解这个问题，我们提出了一个简单的概念驱动的概率模型，以改善基于文本的富媒体搜索。虽然我们的方法类似于现有的基于主题的检索和基于聚类的语言建模工作，但有两个重要的区别：（1）我们提出的模型不仅考虑了来自聚类的查询生成可能性，而且明确地考虑了聚类或底层概念的总体“流行度”，以及（2）我们探索了通过内容所属的用户创建的社区来推断与富媒体内容相关的可能概念的可能性。我们实现了两种方法的概念提取：一个传统的基于聚类的方法，并建议社区为基础的方法。我们评估这两种技术如何有效地捕捉到一个词的预期含义，从内容创建者和搜索引擎，以及他们在改善图像搜索的整体价值。我们的研究结果表明，概念驱动的搜索，虽然简单，明显优于普通搜索。在概念驱动搜索的两种技术中，基于社区的方法更成功，因为从用户社区生成的概念被发现更直观和吸引人。
英文摘要：
Tag-based retrieval of multimedia content is a difficult problem, not only because of the shorter length of tags associated with images and videos, but also due to mismatch in the terminologies used by searcher and content creator. To alleviate this problem, we propose a simple concept-driven probabilistic model for improving text-based rich-media search. While our approach is similar to existing topic-based retrieval and cluster-based language modeling work, there are two important differences: (1) our proposed model considers not only the query-generation likelihood from cluster, but explicitly accounts for the overall "popularity" of the cluster or underlying concept, and (2) we explore the possibility of inferring the likely concept relevant to a rich-media content through the user-created communities that the content belongs to. We implement two methods of concept extraction: a traditional cluster based approach, and the proposed community based approach. We evaluate these two techniques for how effectively they capture the intended meaning of a term from the content creator and searcher, and their overall value in improving image search. Our results show that concept-driven search, though simple, clearly outperforms plain search. Among the two techniques for concept-driven search, community-based approach is more successful, as the concepts generated from user communities are found to be more intuitive and appealing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
