Compact feature based clustering for large-scale image retrieval

Yan Liang, Le Dong, Shanshan Xie, Na Lv, Zongyi Xu
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引用次数: 8

Abstract

This paper addresses the problem of fast similar image retrieval, especially for large-scale datasets with millions of images. We present a new framework which consists of two dependent algorithms. First, a new feature is proposed to represent images, which is dubbed compact feature based clustering (CFC). For each image, we first extract cluster centers of local features, and then calculate distribution histograms of local features and statistics of spatial information in each cluster to form compact features based clustering, replacing thousands of local features. It can reduce feature vectors of image representation and enhance the discriminative power of each feature. In addition, an efficient retrieval method is proposed, based on vocabulary tree through compact features based clustering. Extensive experiments on the Ukbench, Holidays, and ImageNet databases demonstrate that our method reduces the memory and computation overhead and improves the retrieval efficiency, while keeping approximate state-of-the-art accuracy.
基于紧凑特征的聚类大规模图像检索
本文解决了快速相似图像检索的问题,特别是对于具有数百万图像的大规模数据集。我们提出了一个由两个相关算法组成的新框架。首先,提出了一种新的特征来表示图像,该特征被称为基于压缩特征的聚类(CFC)。对于每张图像,我们首先提取局部特征的聚类中心,然后计算局部特征的分布直方图和每个聚类的空间信息统计量,形成基于紧凑特征的聚类,替换数千个局部特征。它可以减少图像表示的特征向量,提高每个特征的判别能力。在此基础上,提出了一种基于词汇树的高效检索方法。在Ukbench、Holidays和ImageNet数据库上进行的大量实验表明,我们的方法减少了内存和计算开销,提高了检索效率,同时保持了接近最先进的精度。
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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