This paper presents a method for recovering and identifying image regions from an initial oversegmentation using qualitative knowledge of its content. Compared to recent works favoring spatial information and quantitative techniques, our approach focuses on simple a priori qualitative inclusion and photometric relationships such as “region A is included in region B”, “the intensity of region A is lower than the one of region B” or “regions A and B depict similar intensities” (photometric uncertainty). The proposed method is based on a two steps' inexact graph matching approach. The first step searches for the best subgraph isomorphism candidate between expected regions and a subset of regions resulting from the initial oversegmentation. Then, remaining segmented regions are progressively merged with appropriate already matched regions, while preserving the coherence with a priori declared relationships. Strengths and weaknesses of the method are studied on various images (grayscale and color), with various initial oversegmentation algorithms (k-means, meanshift, quickshift). Results show the potential of the method to recover, in a reasonable runtime, expected regions, a priori described in a qualitative manner. For further evaluation and comparison purposes, a Python opensource package implementing the method is provided, together with the specifically built experimental database.

中文翻译：

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使用先验定性包含和光度关系的基于图的图像解释方法


本文提出了一种使用图像内容的定性知识从初始过分割中恢复和识别图像区域的方法。与最近偏向空间信息和定量技术的工作相比，我们的方法侧重于简单的先验定性包含和光度关系，例如“区域 A 包含在区域 B 中”、“区域 A 的强度低于区域 B 的强度”或“区域 A 和 B 描绘相似的强度”（光度不确定性）。所提出的方法基于两步不精确图匹配方法。第一步在预期区域和初始过度分割产生的区域子集之间搜索最佳子图同构候选。然后，剩余的分割区域逐渐与适当的已匹配区域合并，同时保持与先验声明的关系的一致性。使用各种初始过分割算法（k-means、meanshift、quickshift）在各种图像（灰度和彩色）上研究该方法的优点和缺点。结果表明该方法有可能在合理的运行时间内恢复预期区域（以定性方式先验描述）。为了进一步评估和比较，提供了实现该方法的Python开源包以及专门构建的实验数据库。