CIF: Small: Theory and Algorithms for Statistical Content Identification

CIF：小：统计内容识别的理论和算法

• 批准号: 1219145
• 负责人: Pierre Moulin
• 金额: $ 44.79万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219145&HistoricalAwards=false
• 关键词: CIF; Small; Theory; Algorithms; Statistical

Automatic Content Identification is an emerging technology that has found applications to broadcast monitoring, connected audio, content tracking, digital asset management, near-duplicate identification, contextual advertising, and as a filtering technology for file sharing. Content identification algorithms must be robust to common signal degradations. They operate on highly compressed data (robust hashes, aka content fingerprints) to meet storage, communication, and computing constraints.The goal of this project is to develop an analytical framework for content identification based upon fundamental principles and modern methods of statistical inference and information theory and to develop novel content identification algorithms. The project focuses on the following four research topics:1. Hash-Based Inference.2. Information-Theoretic Analysis: Content identification is formulated as a communication problem with storage constraints and its fundamental performance limits are investigated.3. Code Design: A learning-theoretic approach is developed for statistical modeling of content fingerprints and degradation channels from training data, and for designing hashing codes and decoding metrics that are optimally matched to these statistics.4. Applications: to audio, images, and video are explored, as well as forensic analysis and security.The project is synergistic and trains graduate students for leadership roles in information technology. The project benefits other areas, including but not limited to content retrieval, clustering, database indexing, pattern recognition, biometrics, and human/computer interaction.

自动内容识别是一项新兴技术，已被应用于广播监控、连接音频、内容跟踪、数字资产管理、近似复制识别、上下文广告以及文件共享的过滤技术。内容识别算法必须对常见的信号退化具有健壮性。这个项目的目标是基于统计推理和信息论的基本原理和现代方法，开发一个内容识别的分析框架，并开发新的内容识别算法。本课题主要围绕以下四个方面展开研究：1.基于哈希的推理。信息论分析：将内容识别问题描述为一个有存储约束的通信问题，并研究了其基本性能极限。代码设计：开发了一种学习理论方法，用于对训练数据中的内容指纹和退化通道进行统计建模，并设计与这些统计最佳匹配的哈希码和解码度量。应用：探索音频、图像和视频，以及法医分析和安全。该项目是协同的，培养信息技术领导角色的研究生。该项目惠及其他领域，包括但不限于内容检索、集群、数据库索引、模式识别、生物识别和人机交互。