ARRA: Identifying Objects Within Scenes: Combining Context and Features in Visual Object Recognition

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。尽管经过近半个世纪的研究，人类视觉识别物体的能力在很大程度上仍然是一个未解之谜。以前对目标识别的研究主要考虑的是孤立观察目标的情况。然而，视觉系统也可以使用上下文信息——比如场景中其他物体的存在，或者关于物体被发现的环境类型的知识——来确定物体的身份。当物体本身的图像不足时，这种信息的贡献尤其明显。例如，在水果盘的上下文中，一个黄色的小块可能被识别为部分模糊的香蕉，或者在树的上下文中被识别为叶子。在一项由美国国家科学基金会资助的研究项目中，佛罗里达大西洋大学的Elan Barenholtz博士将使用行为和计算技术来研究两个关于情境在物体识别中的作用的核心问题：1)人们如何获得关于物体与其情境场景之间关系的知识（例如，特定物体在某种类型的情境中出现的可能性）？2)如何将这些知识应用于识别图像已经退化且无法自行识别的物体？这项研究将采用两种实验方法：第一种方法将使用计算机生成的人工场景，参与者必须首先从头开始学习对象/上下文关系，然后在识别任务中使用这些知识。第二项技术将测试真实世界环境照片中的物体识别能力，包括参与者自己家或工作场所的照片。在这种情况下，受试者将根据他们的长期经验，特别是当环境对他们来说非常熟悉时，对预期的对象/上下文关系有了解。人类在这些任务中的表现将使用统计方法来评估上下文信息在对象识别中的贡献。理解人类的视觉物体识别对脑科学有着巨大的希望——人类皮层的三分之一被认为是专门用于视觉处理的。这种理解对于设计人工视觉系统也很重要，因为它具有大量潜在的应用。然而，以前的理论技术，主要集中在从单个物体中提取三维结构的专门算法上，已经被证明是不成功的。巴伦霍兹博士的研究与早期的方法截然不同，因为它假设视觉识别依赖于推理策略，这种策略利用了个人对世界的广泛了解以及他或她对特定环境的经验。这种方法将视觉视为依赖于与其他认知过程（如推理和决策）类似的工具，这表明在这些不同的学科中可能存在大量以前未被探索的共同点。通过将“认知”回归到“识别”，这项研究有可能为视觉识别领域的一些期待已久的突破做出贡献。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Despite nearly half a century of research, the human ability to recognize objects visually remains a largely unsolved puzzle. Previous research on object recognition has primarily considered cases in which the target is viewed in isolation. However, the visual system can use contextual information -- such as the presence of other objects in the scene or knowledge about the kind of environment in which the object is found -- to determine the identity of an object as well. The contribution of this kind of information is especially clear when the image of the object itself is insufficient on its own. For example, a small yellow patch might be identified as a partially obscured banana in the context of a fruit bowl or as a leaf in the context of a tree. In an NSF-funded research project, Dr. Elan Barenholtz at Florida Atlantic University will use behavioral and computational techniques to examine two central questions regarding the role of context in object recognition: 1) How do people acquire knowledge about the relations between objects and their contextual scenes (for example, the likelihood of specific objects appearing in a certain type of context)? 2) How is this knowledge put to work in recognizing objects whose images have been degraded and cannot be recognized on their own? This research will employ two experimental methodologies: The first will use computer-generated artificial scenes in which participants must first learn the object/context relations from scratch and later use this knowledge in a recognition task. The second technique will test object recognition abilities in photographs of real world environments, including pictures of participants' own homes or workplaces. In this case, subjects will have knowledge about the expected object/context relations, based on their long-term experience, particularly when the environment is highly familiar to them. Human performance in these tasks will be assessed using statistical methods to assess the contribution of contextual information in object recognition.Understanding human visual object recognition holds great promise for brain science -- as much as a third of the human cortex is thought to be devoted to visual processing. Such understanding is also important for designing artificial vision systems, which carry an enormous array of potential applications. However, previous theoretical techniques, which focused on specialized algorithms for extracting 3-dimensional structure from individual objects, have proven largely unsuccessful. Dr. Barenholtz's research represents a strong departure from earlier approaches, as it assumes that visual recognition relies on inferential strategies that draw on an individual's broad knowledge about the world and his or her experience with specific environments. This approach treats vision as relying on similar tools as other cognitive processes, such as inference and decision-making, suggesting that there may be a great deal of previously unexplored common ground across these different disciplines. By putting the "cognition" back into "recognition," this research has the potential to contribute to some long awaited breakthroughs in the field of visual recognition.