基于k近邻的多文本共现描述符图像检索重新排序

Yufis Azhar, A. E. Minarno, Yuda Munarko
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引用次数: 0

摘要

通常用于进行图像检索的特征有颜色、纹理和边缘。多文本共现描述符(MTCD)是一种利用所有三个特征进行图像检索的方法。该方法在对蜡染等图案图像进行检索时具有较高的精度。然而,对于像corel图像这样专注于目标检测的图像,其精度会降低。本研究提出利用KNN方法对MTCD的检索结果进行重新排序,以提高MTCD方法的精度。结果表明,该方法对蜡染图像的精度提高了0.8%,对彩色图像的精度提高了9%。
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Re-Ranking Image Retrieval on Multi Texton Co-Occurrence Descriptor Using K-Nearest Neighbor
Some features commonly used to conduct image retrieval are color, texture and edge. Multi Texton Co-Occurrence Descriptor (MTCD) is a method which uses all three features to perform image retrieval. This method has a high precision when doing retrieval on a patterned image such as Batik images. However, for images focusing on object detection like corel images, its precision decreases. This study proposes the use of KNN method to improve the precision of MTCD method by re-ranking the retrieval results from MTCD. The results show that the method is able to increase the precision by 0.8% for Batik images and 9% for corel images.
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