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首页> 外文期刊>International Journal of Grid and Utility Computing >A novel web image retrieval method: bagging weighted hashing based on local structure information
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A novel web image retrieval method: bagging weighted hashing based on local structure information

机译：一种新颖的网络图像检索方法：基于局部结构信息的袋装加权哈希

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摘要

Hashing is widely used in ANN searching problems, especially in web image retrieval. An excellent hashing algorithm can help the users to search and retrieve their web images more conveniently, quickly and accurately. In order to conquer several deficiencies of ITQ in image retrieval problems, we use ensemble learning to solve them. An elastic ensemble framework has been proposed to guide the hashing design, and three important principles have been proposed, named high precision, high diversity, and optimal weight prediction. Based on this, we design a novel hashing method called BWLH. In BWLH, first, the local structure information of the original data is extracted to construct the local structure data, thus to improve the similarity-preserve ability of hash bits. Second, a weighted matrix is used to balance the variance of different bits. Third, bagging is exploited to expand diversity in different hash tables. Sufficient experiments show that BWLH can handle image retrieval problems effectively, and perform better than several state-of-the-art methods at same hash code length on dataset CIFAR-10 and LabelMe. Finally, 'search by image', a web-based use-case scenario of the proposed hashing BWLH is given to detail how the proposed method can be used in a web-based environment.
机译：散列广泛用于ANN搜索问题，尤其是在Web图像检索中。出色的哈希算法可以帮助用户更方便，快速，准确地搜索和检索其Web图像。为了克服ITQ在图像检索问题上的一些不足，我们使用集成学习来解决它们。提出了一种弹性集合框架来指导哈希设计，并提出了三个重要的原理，即高精度，高分集和最佳权重预测。基于此，我们设计了一种称为BWLH的新型哈希方法。在BWLH中，首先，提取原始数据的局部结构信息以构造局部结构数据，从而提高哈希位的相似性保持能力。其次，使用加权矩阵来平衡不同位的方差。第三，利用袋装来扩展不同哈希表中的多样性。充分的实验表明，在数据集CIFAR-10和LabelMe上，在相同的哈希码长度下，BWLH可以有效地处理图像检索问题，并且比几种最新的方法表现更好。最后，给出了“按图像搜索”，即所提出的哈希BWLH的基于Web的用例场景，以详细说明如何在基于Web的环境中使用所提出的方法。

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