Large-vocabulary Semantic Image Processing: Theory and Algorithms

大词汇量语义图像处理：理论与算法

• 批准号: 0830535
• 负责人: Nuno Vasconcelos
• 金额: $ 23.95万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830535&HistoricalAwards=false
• 关键词: Large; vocabulary; Semantic; Image; Processing

Classical image processing has mostly disregarded semantic image representations, in favor of more mathematically tractable representations based on low?]level signal properties (frequency decompositions, mean squared error, etc.). This is unlike biological solutions to image processingproblems, which rely extensively on understanding of scene content. For example, regions of faces are usually processed more carefully than the bushes in the background. The inability to tune image processing to the semantic relevance of image content frequently leads to the sub?]optimal allocation ofresources, such as bandwidth, error protection, or viewing time, to image areas that are perceptually irrelevant. One of the main obstacles to the deployment of semantic image processing systems has been the difficulty of training content?]understanding systems with large scale vocabularies. This is, in great part, due to the requirement for large amounts of training data and intensive human supervision associated with the classical methods for vocabulary learning. This research aims to establish a foundation for semantic image processing systems that can learn large scale vocabularies frominformally annotated data and no additional human supervision. It builds on recent advances in semantic image labeling, which have made it possible to learn vocabularies from noisy training data, such as that massively (and inexpensively) available on the web. The research studies both theoreticalissues in vocabulary learning, and the design of image processing algorithms that tune their behavior according to the content of the images being processed. Semantic image processing could lead to transformative advances in areas such as image compression, enhancement, encryption, de?]noising, orsegmentation, among others, which are of interest for applications as diverse as medical imaging, image search and retrieval, or security and surveillance.

经典图像处理大多忽略了语义图像表示，以基于低的信号属性（频率分解，平均平方错误等）更有利于数学上可触犯的表示。这与图像处理问题的生物解决方案不同，该解决方案广泛依赖于对场景内容的理解。例如，通常比背景中的灌木丛更谨慎地处理面部区域。无法调整图像处理图像内容的语义相关性通常会导致子？]最佳分配，例如带宽，错误保护或查看时间，到感知上无关紧要的图像区域。语义图像处理系统部署的主要障碍之一是训练内容的困难吗？]了解具有大型词汇的系统。在很大程度上，这是由于需要大量培训数据以及与词汇学习的经典方法相关的大量培训数据。这项研究旨在为语义图像处理系统建立基础，该系统可以从文献注释的数据中学习大规模词汇，而没有其他人类的监督。它以语义图像标签的最新进展为基础，这使得从嘈杂的训练数据中学习词汇是可能的，例如在网络上可以大量（和便宜）。研究研究词汇学习中的理论上以及根据所处理图像的内容调整其行为的图像处理算法的设计。语义图像处理可能会导致在图像压缩，增强，加密，de？] Noising，de sementation等领域的变革性进步，这些应用程序对于像医学成像，图像搜索和检索或安全性和安全性和监视的应用一样多样化。