Feature fusion based hashing for large scale image copy detection

Lingyu Yan, H. Ling, Dengpan Ye, Chunzhi Wang, Z. Ye, Hongwe Chen
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引用次数: 7

Abstract

Most of existing approaches use only a single feature to represent an image for copy detection. However, a single feature is often insufficient to characterize the image content. Besides, with the exponential growth of online images, it's urgent to explore a way of tackling the problem of large scale. In this paper, we propose a feature fusion based hashing method which effectively utilize the correlation between two feature models and efficiently accomplish large scale image copy detection. To accurately map images into the Hamming space, our hashing method not only preserves the local structure of individual feature but also globally consider the local structures for all the features to learn a group of hash functions. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
基于特征融合的哈希算法用于大规模图像复制检测
现有的大多数方法仅使用单个特征来表示图像以进行复制检测。然而,单一的特征往往不足以表征图像内容。此外,随着网络图像的指数级增长,迫切需要探索一种解决大规模问题的方法。本文提出了一种基于特征融合的哈希方法,该方法有效地利用了两个特征模型之间的相关性,有效地完成了大规模图像复制检测。为了准确地将图像映射到汉明空间中,我们的哈希方法不仅保留了单个特征的局部结构,而且全局考虑了所有特征的局部结构来学习一组哈希函数。实验结果表明,该方法在精度和效率上都优于现有的方法。
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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