Collaborative Research: Randomized Invariant Features for Recognition

加州大学圣克鲁斯分校和杜克大学的这项合作提案的目标是定义用于识别和分类图像中对象的特征。当特征在预定义的类别中经历所有可能的变换时，通过对获得的特征空间流形进行有效的、概率的编码，丰富了特征向量并同时使其不变。这种编码旨在通过将表示要求降低到识别和分类所必需的两个属性，即唯一性和连续性，来规避维度诅咒。唯一性要求不同的流形进行不同的编码，而连续性要求相似流形的编码相似。理论和经验研究将研究最优特征设计，这些特征在不同分类和识别方法下的性能，以及计算效率和性能之间的权衡。还将通过理论界限和经验测试来衡量特征和变换家族的错分率。建议表示的行为将在存在杂乱和数据损坏(如遮挡)的情况下进行检查。这些概念将通过它们在计算机断层扫描中对结肠癌的自动诊断以及从视频序列中解读美国手语的应用来测试。

The goal of this collaborative proposal between the University of California at Santa Cruz and Duke University is the definition of features for recognition and classification of objects in imagery. Feature vectors are enriched and made invariant at the same time by an efficient, probabilistic coding of the feature-space manifolds that are obtained when features undergo all possible transformations in a predefined class. This coding is designed to circumvent the curse of dimensionality by reducing the representation requirements to the two properties that are essential for recognition and classification, that is, uniqueness and continuity. Uniqueness requires different manifolds to be coded differently, while continuity requires similar codings for similar manifolds.Theoretical and empirical investigations will study optimal feature design, the performance of these features with various classification and recognition methods, and the trade-off of computational efficiency and performance. Misclassification rates for families of features and transformations will also be measured, both by theoretical bounds and empirical tests. The behavior of the proposed representations will be examined in the presence of clutter and data corruptions such as occlusions. These concepts will be tested through their application to the automatic diagnosis of colon cancer from computerized tomography scans, and to the interpretation of American sign language from video sequences.