面向MARS多媒体相似度检索的查询细化

Kriengkrai Porkaew, K. Chakrabarti
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引用次数: 194

摘要

在过去的几年里,基于内容的多媒体检索已成为最活跃的研究领域之一。与传统的数据库查询不同,基于内容的多媒体检索查询在本质上是不精确的,这使得用户无法立即以精确查询的形式表达其准确的信息需求。典型的界面允许用户通过选择与她希望检索的对象相似的对象示例来表达她的信息需求。这样的用户界面需要从示例中学习查询表示的机制。在本文中,我们提出了在多媒体分析与检索系统(MARS)中使用的查询重构方法,通过相关反馈来学习查询表示。提出的技术使用查询扩展来修改查询表示。在查询扩展中,在反馈的每次迭代中,将相关对象添加到查询中,并删除不相关的对象。我们将其与之前工作中提出的基于查询点移动的方法进行比较。我们提出了在MARS中处理相似查询和关联查询的客户查询评估技术。我们的实验表明,查询扩展在检索效率和执行成本方面都明显优于查询点移动方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Query refinement for multimedia similarity retrieval in MARS
During the past few years, content-based multimedia retrieval has become one of the most active areas of research. Unlike traditional database queries, content-based multimedia retrieval queries are imprecise in nature which makes it di cult for users to express their exact information need in the form of a precise query right away. A typical interface allows the user to express her information need by selecting examples of objects similar to the ones she wishes to retrieve. Such a user interface requires mechanisms to learn the query representation from the examples. In this paper, we present the query re nement approach used in the Multimedia Analysis and Retrieval System (MARS) for learning query representations through relevance feedback. The proposed technique uses query expansion towards modifying the query representation. In query expansion, in each iteration of feedback, the relevant objects are added to the query and non-relevant ones are removed. We compare it with approaches based on query point movement proposed in our previous work. We propose e cient query evaluation techniques for processing similarity queries and re ned queries in MARS. Our experiments show that query expansion signi cantly outperforms the query point movement approach in both in terms of retrieval e ectiveness and execution cost.
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