Abstract

Apart from machine learning and knowledge engineering, there is a third way of challenging machine vision – the Gestalt-law school. In an interdisciplinary effort between psychology and cybernetics, compositionality in perception has been studied for at least a century along these lines. Hierarchical compositions of parts and aggregates are possible in this approach. This is particularly required for high-quality high-resolution imagery becoming more and more common, because tiny details may be important as well as large-scale interdependency over several thousand pixels distance. The contribution at hand studies the depth of Gestalt-hierarchies in typical social image genres—portraits and group pictures—exemplarily, and outlines technical means for their automatic extraction. The practical part applies bottom-up hierarchical Gestalt grouping as well as top-down search focusing and constraint enforcement, listing as well success as failure. In doing so, the paper discusses exemplarily the depth and nature of such compositions in imagery relevant to human beings.

中文翻译：

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社会意象中格式塔层次的深度探索

摘要

除了机器学习和知识工程，还有第三种挑战机器视觉的方式——格式塔法学院。在心理学和控制论之间的跨学科努力中，至少一个世纪以来，人们已经沿着这些方向研究了感知的组合性。在这种方法中，部分和集合的分层组合是可能的。这对于变得越来越普遍的高质量高分辨率图像尤其需要，因为微小的细节可能很重要，而且数千像素距离上的大规模相互依赖性也很重要。手头的贡献以典型的社会图像类型——肖像和集体图片——为例研究了格式塔层次结构的深度，并概述了它们自动提取的技术手段。实践部分应用自下而上的分层格式塔分组以及自上而下的搜索聚焦和约束执行，列出成功与失败。在此过程中，本文示例性地讨论了与人类相关的图像中此类构图的深度和性质。