Seebibyte: Visual Search for the Era of Big Data

该计划分为两个主题。研究主题一将开发新的计算机视觉算法，以实现对海量图像和视频数据集的高效搜索和描述-例如，BBC的整个视频档案。我们的愿景是，任何可视化的东西都应该可以搜索，就像谷歌搜索网络一样：通过指定一个查询，并立即返回结果，而不考虑数据的大小。这种使能能力将广泛应用于一般图像/视频搜索--想想谷歌的网络搜索如何开辟了新的领域--也将用于设计定制的搜索解决方案。主题1的第二个方面是自动提取视觉内容的详细描述。这里的目标是实现与人类相似的性能和更远的性能，例如在识别部件和空间布局的配置、计数和描绘对象、或识别视频中的人类动作和交互动作方面，显著取代当前计算机视觉系统的限制，并实现新的和深远的应用。新的算法将自动学习，建立在最近大规模区分和深度机器学习的突破的基础上。它们将能够进行弱监督学习，例如从互联网下载的图像和视频，并且需要很少的人工监督。第二个主题涉及传输和翻译。这也有两个方面。第一种是将新的计算机视觉方法应用于“非天然”传感器和设备，如超声波成像和X射线，它们与“天然”相机(iPhone、电视摄像机)捕获的标准RGB通道数据具有不同的特征(噪声、尺寸、不变性)。这个主题的第二个方面是寻求在各种其他学科和行业中产生影响，这些学科和行业今天大大低估了最新计算机视觉思想的力量。我们将针对这些学科，使它们能够跨越目前以人工审查和高度交互的逐帧分析为主的使用(或不使用)之间的鸿沟，进入一个对超大数据集进行自动高效分类、检测和测量成为常态的新时代。简而言之，我们的目标是确保新开发的方法被其他领域的学术研究人员使用，并转化为具有社会效益和经济效益的产品。为此，开源软件、数据集和演示程序将在项目网站上传播。数字成像的无处不在意味着每个英国公民都可能以不同的方式从该计划的研究中受益。一个例子是增强型iPlayer，它可以搜索节目中特定角色出现的位置，或者智能地快进到下一个拥抱序列。第二是在医疗保健服务中更广泛地部署成本较低的成像解决方案。第三个，也是受医疗保健的推动，是通过使用新的机器学习方法来验证基于显微镜图像的药物发现目标

The Programme is organised into two themes. Research theme one will develop new computer vision algorithms to enable efficient search and description of vast image and video datasets - for example of the entire video archive of the BBC. Our vision is that anything visual should be searchable for, in the manner of a Google search of the web: by specifying a query, and having results returned immediately, irrespective of the size of the data. Such enabling capabilities will have widespread application both for general image/video search - consider how Google's web search has opened up new areas - and also for designing customized solutions for searching.A second aspect of theme 1 is to automatically extract detailed descriptions of the visual content. The aim here is to achieve human like performance and beyond, for example in recognizing configurations of parts and spatial layout, counting and delineating objects, or recognizing human actions and inter-actions in videos, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications. The new algorithms will learn automatically, building on recent breakthroughs in large scale discriminative and deep machine learning. They will be capable of weakly-supervised learning, for example from images and videos downloaded from the internet, and require very little human supervision.The second theme addresses transfer and translation. This also has two aspects. The first is to apply the new computer vision methodologies to `non-natural' sensors and devices, such as ultrasound imaging and X-ray, which have different characteristics (noise, dimension, invariances) to the standard RGB channels of data captured by `natural' cameras (iphones, TV cameras). The second aspect of this theme is to seek impact in a variety of other disciplines and industry which today greatly under-utilise the power of the latest computer vision ideas. We will target these disciplines to enable them to leapfrog the divide between what they use (or do not use) today which is dominated by manual review and highly interactive analysis frame-by-frame, to a new era where automated efficient sorting, detection and mensuration of very large datasets becomes the norm. In short, our goal is to ensure that the newly developed methods are used by academic researchers in other areas, and turned into products for societal and economic benefit. To this end open source software, datasets, and demonstrators will be disseminated on the project website.The ubiquity of digital imaging means that every UK citizen may potentially benefit from the Programme research in different ways. One example is an enhanced iplayer that can search for where particular characters appear in a programme, or intelligently fast forward to the next `hugging' sequence. A second is wider deployment of lower cost imaging solutions in healthcare delivery. A third, also motivated by healthcare, is through the employment of new machine learning methods for validating targets for drug discovery based on microscopy images