Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features
Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features
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DOI：
10.1007/978-3-319-16634-6_5
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发表时间：
2014-11
影响因子：
1.8
通讯作者：
Kazuaki Nakamura;N. Babaguchi
中科院分区：
文献类型：
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作者：
Kazuaki Nakamura;N. Babaguchi
现有的测量图像实例中两个概念之间的概念间距离(ICD)的方法大多只使用从每个实例中提取的单一类型的视觉特征。然而，单一类型的特征不足以适当地测量ICD，因为用于相似性评估的视角多种多样(例如，颜色、形状、大小、硬度、重量和功能)；不同概念对之间的关系更适合从由多种特征提供的不同视角建模。在本文中，我们建议从每个图像实例中提取两种或两种以上的视觉特征，并利用这些特征来测量ICD。此外，我们还提出了一种基于视觉特征对每个概念对的适宜性自适应加权的方法。实验结果表明，该方法优于仅使用单一视觉特征的方法和多种特征加固定权值的方法。
Most of the existing methods for measuring the inter-concept distance (ICD) between two concepts from their image instances use only a single kind of visual feature extracted from each instance. However, a single kind of feature is not enough for appropriately measuring ICDs due to a wide variety of perspectives for similarity evaluation (e.g., color, shape, size, hardness, heaviness, and functions); the relationships between different concept pairs are more appropriately modeled from different perspectives provided by multiple kinds of features. In this paper, we propose extracting two or more kinds of visual features from each image instance and measuring ICDs using these multiple features. Moreover, we present a method for adaptively weighting the visual features on the basis of their appropriateness for each concept pair. Experiments demonstrated that the proposed method outperformed a method using only a single kind of visual feature and one combining multiple kinds of features with a fixed weight.