A Semantic-Similarity-Based Method for Object Description and Clustering
A Semantic-Similarity-Based Method for Object Description and Clustering
复制标题
一种基于语义相似性的对象描述和聚类方法
DOI：
10.1109/smc.2013.625
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Katsumi Nitta
中科院分区：
文献类型：
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作者：
Jing Xu;Shogo Okada;Katsumi Nitta
目标识别和聚类是模式识别和计算机视觉中的重要技术。传统上，这些技术已经实现了基于视觉特征的方法。然而，这些方法可能无法充分解决对象的形状和颜色的差异。在本文中，我们提出了一种替代方法，其中不同颜色，甚至不同形状的对象，功能相似。如果文本字符串在它们的表面上可见，我们可以提取对象的语义特征，从而识别和聚类它们。因此，该方法是基于语义信息的。该方法进行了实验测试，包含商业产品的包装箱的图像数据集。使用文本提取模块检索数据集图像中的语义信息，通过互联网数据挖掘模块，并最终进行描述和聚类。最终的聚类结果比基于视觉特征的方法更准确。
Object recognition and clustering are useful techniques in pattern recognition and computer vision. Traditionally, these techniques have been implemented by visual-feature-based methods. However, these methods may not adequately tackle the differences in the shapes and colors of objects. In this paper, we propose an alternative method in which objects of different colors, or even different shapes, function similarly. If text strings are visible on their surfaces, we can extract the semantic features of objects, thereby recognizing and clustering them. Thus, this method is based on semantic information. The method is experimentally tested on a dataset of images containing the packing cases of commercial products. Semantic information in the dataset images is retrieved using text extraction modules, passed through an Internet data mining module and is finally described and clustered. The final clustering results are more accurate than those obtained by visual-feature-based methods.