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A visual scene busyness measure through a region growing spatial segmentation

机译：通过增长空间分割的区域的视觉场景繁忙度测量

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This paper proposes a visual scene busyness indicator obtained from the properties of a full spatial segmentation of static images. A fast and effective region merging scheme is applied for this purpose. It uses a semi-greedy merging criterion and an adaptive threshold to control segmentation resolution. The core of the framework is a hierarchical parallel merging model and region reduction techniques. The segmentation procedure consists of the following phases: 1. algorithmic region merging, and 2. region reduction, which includes small segment reduction and enclosed region absorption. Quantitative analyses on standard benchmark data have shown the procedure to compare favourably to other segmentation methods. Qualitative assessment of the segmentation results indicate approximate semantic correlations between segmented regions and real world objects. This characteristic is used as a basis for quantifying scene busyness in terms of properties of the segmentation map and the segmentation process that generates it. A visual busyness indicator based on full colour segmentation is evaluated against conventional measures.
机译：本文提出了一种从静态图像的完整空间分割的特性获得的视觉场景繁忙指示器。为此目的应用快速有效的区域合并方案。它使用半贪婪合并标准和自适应阈值来控制分割分辨率。框架的核心是分层并行合并模型和区域减少技术。分割程序包括以下阶段：1。算法区域合并，以及2.区域减少，包括小段减少和封闭区域吸收。标准基准数据的定量分析已经显示了对其他分段方法有利的过程。分割结果的定性评估表明分段地区和现实世界对象之间的近似语义相关性。在分割图的属性和生成它的分割过程中，将该特性用作量化场景繁忙度的基础。评估了基于全彩分割的视觉繁忙指示器针对传统措施。

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