Inducing Features from Visual Noise using Statistical Machine Learning Techniques

对于想要理解视觉系统的心理学家和想要模拟视觉系统能力的计算机科学家来说，理解人类如何区分视觉对象是一个核心问题。以前的工作已经开发了确定图像位置和空间频率的技术，这些技术对人类在分类和识别任务中的表现最为重要。然而，这些技术不能告诉我们是否在单个步骤中检测到重要的图像特征，例如，通过将视觉对象与单个内部表示（如模板）相匹配，或通过组合多个检测器的结果。在美国国家科学基金会的支持下，马萨诸塞大学阿默斯特分校的安德鲁·科恩博士将开发一种新的模型，即多独立模板诱导模型（MITIM），旨在回答这些问题。人类行为实验的任务，如在嘈杂的图像中识别单词，表明人们并不总是从整体上识别物体。相反，它们似乎是独立检测碎片，然后将这些结果结合起来。如果这在人脸或物体分类和识别等领域是正确的，那么理解这些能力需要了解独立检测的部分及其相对重要性。提出的MITIM算法将使用人工智能研究中的统计技术来处理人类图像分类数据，并发现用于对象识别的独立模板。所获得的见解将有助于阐明人类的视觉表现，并帮助科学家和工程师更好地理解如何构建计算机系统，以复制在网络搜索、机器人和安全应用中日益重要的性能和功能。该奖项是2006财政年度数学科学优先领域数学社会和行为科学特别竞赛的一部分。

Understanding how humans differentiate between visual objects is a central problem for psychologists who want to understand the visual system and for computer scientists who want to emulate its abilities. Previous work has developed techniques for determining the image locations and spatial frequencies most responsible for human performance in classification and identification tasks. These techniques, however, cannot tell us if the important image features are detected in a single step, for example, by matching the visual object to a single internal representation such as a template, or by combining the result of multiple detectors. With support from the National Science Foundation, Dr. Andrew Cohen from the University of Massachusetts, Amherst, will develop a new model, the Multiple Independent Template Induction Model (MITIM), designed to answer such questions.Human behavioral experiments on tasks such as word recognition in noisy images have suggested that people do not always recognize objects holistically. Rather, they appear detect pieces independently and then combine those results. If this is true in domains such as face or object classification and recognition, understanding these abilities requires knowledge of the independently detected parts and their relative importance. The proposed MITIM algorithm will use statistical techniques from artificial intelligence research to process human image classification data and discover the independent templates used in object recognition. The insights gained will help illuminate human visual performance and help scientists and engineers better understand how to build computer systems that can replicate that performance, functionality that is increasingly important in web search, robotic, and security applications.This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).