Content-based surface material retrieval
Content-based surface material retrieval
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DOI：
10.1109/whc.2017.7989927
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Matti Strese;Yannik N. Böck;E. Steinbach
中科院分区：
文献类型：
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作者：
Matti Strese;Yannik N. Böck;E. Steinbach
我们提出了一个基于内容的表面材料检索（CBSMR）系统的工具介导的徒手表面探索，依赖于触觉表面纹理感知的主要心理物理维度的动机的功能。所提出的方法不需要明确的扫描力和扫描速度测量。在我们的CBSMR引擎中使用的感知特征涵盖了摩擦，硬度，宏观粗糙度，微观粗糙度和温暖的触觉尺寸。我们研究了108个表面材料记录不同的用户，并提出了一个自由排序分组实验的结果，我们进行了30个主题，以确定我们的数据库中的表面材料的感知相似性，感知触觉相似性提供了地面实况数据集。这个实验的结果是用来证明，建议CBSMR引擎是能够确定感知最相似的表面材料的测试查询。建议的8个特征的集合导致86%的分类精度和30%的相似性精度，在一个基于欧氏距离的分类器相结合时。
We present a content-based surface material retrieval (CBSMR) system for tool-mediated freehand surface exploration that relies on features motivated by the main psychophysical dimensions of tactile surface texture perception. The proposed approach does not require explicit scan force and scan velocity measurements. The perceptual features used in our CBSMR engine cover the tactile dimensions of friction, hardness, macroscopic roughness, microscopic roughness and warmth. We examine 108 surface materials recorded by different users and present the results of a free-sorting grouping experiment with 30 subjects which we conducted to determine the perceptual similarity of the surface materials in our database, providing a ground truth data set for perceived tactile similarity. The outcome of this experiment is used to demonstrate that the proposed CBSMR engine is able to determine the perceptually most similar surface materials for a test query. The proposed set of 8 features leads to a classification precision of 86% and a similarity precision-at-one of 30% when combined with a Euclidean Distance-based classifier.