Hierarchical product quantization for effective feature indexing

Van-Hao Le, T. Pham, Dinh-Nghiep Le
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Abstract

Feature indexing is a critical technique for addressing real-time image matching and retrieval. In this work, we propose a novel quantization method that is capable of creating highly accurate quantized codes for a given feature database. Differing from many quantization techniques in the literature (typically, product quantization based methods), the proposed method is designed to reshape the feature vectors so that close points are placed into a small sub-space. To this aim, a hierarchical product quantization method is presented. In its essence, the first level of quantization aims at reordering the dimensions so as to exploit better the correlation among subspaces. The second level of quantization is then invoked to create a sub-quantizer for the points contained in each sub-space. To validate the proposed method, various experiments have been conducted, demonstrating quite impressive performance when compared with other state-of-the-art methods.
有效特征索引的分层产品量化
特征索引是解决实时图像匹配和检索的关键技术。在这项工作中,我们提出了一种新的量化方法,能够为给定的特征数据库创建高度精确的量化代码。与文献中的许多量化技术(通常是基于积量化的方法)不同,本文提出的方法旨在重塑特征向量,以便将接近的点放置在小的子空间中。为此,提出了一种层次积量化方法。从本质上讲,第一级量化的目的是对维度进行重新排序,以便更好地利用子空间之间的相关性。然后调用第二级量化来为每个子空间中包含的点创建子量化器。为了验证所提出的方法,已经进行了各种实验,与其他最先进的方法相比,显示出相当令人印象深刻的性能。
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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