摘要翻译：
特征加权是一种用来近似单个特征的最佳影响程度的技术。提出了一种基于关键词定位的文档图像检索系统(DIRS)特征加权方法。该方法利用多重相关系数对特征进行加权。多重相关系数可以用来描述各特征的综合效果和相关性。本文的目的是表明特征加权提高了DIRS的性能。将特征加权方法应用于DIRS后，平均查准率为93.23%，平均查全率为98.66%
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英文标题：
《Feature Weighting for Improving Document Image Retrieval System
  Performance》
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作者：
Mohammadreza Keyvanpour, Reza Tavoli
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the feature using coefficient of multiple correlations. Coefficient of multiple correlations can be used to describe the synthesized effects and correlation of each feature. The aim of this paper is to show that feature weighting increases the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 93.23% and average recall become 98.66% respectively
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PDF链接：
https://arxiv.org/pdf/1206.1291