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Color pattern selection using linguistic image scales

机译:使用语言图像比例尺选择颜色模式

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Abstract: In this paper, we propose a novel scheme to construct the color pattern retrieval system, which can recommend color patterns of the desired human feeling. The desired feeling is represented as a 9D vector in linguistic image scales. That means a component value of the vector is the degree of the feeling in the corresponding linguistic image scale. Then the system recommends color patterns of desired feeling after it compares the given query vector with emotional features of stored color patterns. In order to construct the system, the emotional features are taken from outputs of the neural network which has inputs as the physical features extracted from color patterns. To make indexing system, the hierarchical clustering method with fuzzy c-mean algorithm is used. To verify our scheme, a set of 368 color images for textile design is selected and experimented. Using the proposed retrieval scheme, we could obtain a promising results even though several problems are still remained. We believe this pilot system can be improved to find corresponding textile designs, wall papers, or pictures in a gallery for linguistic queries of human feelings.!15
机译:摘要:在本文中,我们提出了一种新颖的方案来构造颜色图案检索系统,该系统可以推荐所需的人类感觉的颜色图案。期望的感觉以语言图像比例表示为9D向量。这意味着矢量的分量值是相应语言图像比例中的感觉程度。然后,系统在将给定的查询向量与存储的颜色模式的情感特征进行比较之后,推荐所需感觉的颜色模式。为了构建系统,从神经网络的输出中获取情感特征,该神经网络的输入具有从颜色模式中提取的物理特征。为了建立索引系统,使用了基于模糊c均值算法的层次聚类方法。为了验证我们的方案,选择并试验了一组368幅用于纺织设计的彩色图像。使用提出的检索方案,即使仍然存在一些问题,我们也可以获得令人满意的结果。我们相信,可以改进此试验系统,以便在画廊中找到相应的纺织品设计,墙纸或图片,以对人类的情感进行语言查询。15

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