CIF: Small: Theory and Algorithms for Statistical Content Identification

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、近重复识别、上下文广告，以及作为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鲁棒性。它们对高度压缩的数据（鲁棒散列，又称内容指纹）进行操作，以满足存储、通信和计算约束。该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。本项目主要围绕以下四个研究课题展开：1。基于散列的Inference.2。信息论分析：将内容识别表述为具有存储约束的通信问题，并研究其基本性能限制。代码设计：开发了一种学习理论方法，用于从训练数据中对内容指纹和退化通道进行统计建模，并用于设计与这些统计数据最佳匹配的散列代码和解码度量。应用：音频，图像和视频的探索，以及法医分析和安全。该项目是协同的，并培养研究生在信息技术的领导角色。该项目有利于其他领域，包括但不限于内容检索、聚类、数据库索引、模式识别、生物识别和人机交互。

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.