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A visual scene busyness measure through a region growing spatial segmentation

机译：通过区域增长的空间分割进行视觉场景繁忙度测量

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摘要

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全空间分割的属性获得的视觉场景繁忙度指标。为此目的采用了快速有效的区域合并方案。它使用半贪婪合并准则和自适应阈值来控制分割分辨率。该框架的核心是分层并行合并模型和区域缩减技术。分割过程包括以下阶段：1.算法区域合并，和2.区域缩小，其中包括小段缩小和封闭区域吸收。对标准基准数据的定量分析表明，该程序可与其他细分方法进行比较。对分割结果的定性评估表明，分割区域与现实世界对象之间存在近似的语义相关性。根据分割图的属性和生成该图的分割过程，此特性用作量化场景繁忙度的基础。基于常规色彩评估基于全色分割的视觉繁忙度指标。

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