East Nusa Tenggara Weaving Image Retrieval Using Convolutional Neural Network

Silvester Tena, Rudy Hartanto, I. Ardiyanto
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引用次数: 2

Abstract

The popularity of East Nusa Tenggara (ENT) province is attributed to a variety of traditional woven fabrics with local cultural attributes. Each tribe in the province has its design and colors that differentiate the fabrics leading to diverse decorative motifs. Due to different varieties, it is challenging for users to know both the type of motif and its origins. In this research, several Convolutional neural network (CNN) architecture benchmarks were carried out for ENT weaving images retrieval. The image retrieval method was chosen for the study since it has feature extraction and similarity measurement, which make searching and selection relatively easier. Furthermore, the CNN method is often used for feature extraction due to its ability to recognize objects while hashing and hamming distance algorithms help reduce the computation time for similarity testing. This study was conducted by comparing several pre-trained CNN architectures, including VGG16, ResNet101, InceptionV3, and Discrete Wavelet Transform. The results showed that the highest accuracy is ResNet101 architecture with 100%, 88.50%, and 55% at top=1, top=5, and top=10, respectively. The pre-trained CNN model and Discrete Wavelet Transform combination provided better results in case the feature dimensions were above 16-bit. The feature dimensions are generally based on the best 6-bit hashing code, though they are computationally time-consuming.
基于卷积神经网络的东努沙登加拉织造图像检索
东努沙登加拉省(ENT)的受欢迎程度归功于各种具有当地文化属性的传统机织织物。该省的每个部落都有自己的设计和颜色,以区分面料,从而产生不同的装饰图案。由于图案种类繁多,使用者很难了解图案的类型和起源。在本研究中,对几种卷积神经网络(CNN)架构进行了测试,用于耳鼻喉科编织图像检索。选择图像检索方法进行研究,因为它具有特征提取和相似度度量,使得搜索和选择相对容易。此外,CNN方法由于其识别对象的能力而经常用于特征提取,而哈希和汉明距离算法有助于减少相似性测试的计算时间。本研究通过比较几种预训练的CNN架构,包括VGG16、ResNet101、InceptionV3和离散小波变换。结果表明,在top=1、top=5和top=10时,ResNet101架构的准确率最高,分别为100%、88.50%和55%。预训练的CNN模型和离散小波变换组合在特征维数大于16位的情况下效果更好。特征维度通常基于最好的6位散列代码,尽管它们在计算上很耗时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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