Image Retrieval of Indonesian Batik Clothing Based on Convolutional Neural Network

Mutia Fadhilla, Des Suryani, Nesi Syafitri, Hendra Gunawan
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引用次数: 1

Abstract

Indonesian Batik is best-known for unique and distinct pattern. Searching Indonesian Batik clothing images is a challenging problem due to its wide pattern variations. In this paper, proposed image retrieval model of Indonesian Batik clothing image searching based on Convolutional Neural Network (CNN). Autoencoder proposed as CNN model that trained to reconstructed original input batik clothing image. So, the visual features can be extracted from CNN Autoencoder. Based on the experimental results, the proposed method can reach 90.8% in retrieval accuracy, 58.8% in mean average precision, and 61.9% in average recall.
基于卷积神经网络的印尼蜡染服装图像检索
印尼蜡染以其独特的图案而闻名。印尼蜡染服装图像的搜索是一个具有挑战性的问题,因为它的图案变化很大。本文提出了一种基于卷积神经网络(CNN)的印尼蜡染服装图像检索模型。提出自编码器作为CNN模型,训练重建原始输入蜡染服装图像。因此,可以从CNN自动编码器中提取视觉特征。实验结果表明,该方法的检索准确率为90.8%,平均精密度为58.8%,平均查全率为61.9%。
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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