Lost in binarization: query-adaptive ranking for similar image search with compact codes
Lost in binarization: query-adaptive ranking for similar image search with compact codes
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DOI：
10.1145/1991996.1992012
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发表时间：
2011-04
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通讯作者：
Yu-Gang Jiang;Jun Wang;Shih-Fu Chang
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文献类型：
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作者：
Yu-Gang Jiang;Jun Wang;Shih-Fu Chang
随着Web上图像的激增，视觉相似图像的快速搜索已经引起了极大的关注。现有技术通常将高维视觉特征嵌入到低维汉明空间中，其中可以基于紧凑二进制码的汉明距离实时执行搜索。与传统的度量（例如，原始图像特征的汉明距离是连续的，而汉明距离是离散的整数值。在实践中，经常有大量的图像共享相同的汉明距离的查询，导致在图像搜索中的排名是非常重要的一个关键问题。在本文中，我们提出了一种新的方法，有利于查询自适应排名的图像具有相等的汉明距离。我们实现了这一目标，首先离线学习的二进制代码的位权重为一组不同的预定义的语义概念类。权重学习过程被公式化为一个二次规划问题，最小化类内距离，同时保持原始图像特征空间中的类间关系。查询自适应权重，然后快速计算通过评估查询和概念类别之间的接近度。利用自适应位权重，可以在更细粒度的二进制代码级别而不是在原始整数汉明距离级别上通过加权汉明距离对返回的图像进行排序。Flickr图像数据集上的实验结果表明，我们的查询自适应排名方法有明显的改善。
With the proliferation of images on the Web, fast search of visually similar images has attracted significant attention. State-of-the-art techniques often embed high-dimensional visual features into low-dimensional Hamming space, where search can be performed in real-time based on Hamming distance of compact binary codes. Unlike traditional metrics (e.g., Euclidean) of raw image features that produce continuous distance, the Hamming distances are discrete integer values. In practice, there are often a large number of images sharing equal Hamming distances to a query, resulting in a critical issue for image search where ranking is very important. In this paper, we propose a novel approach that facilitates query-adaptive ranking for the images with equal Hamming distance. We achieve this goal by firstly offline learning bit weights of the binary codes for a diverse set of predefined semantic concept classes. The weight learning process is formulated as a quadratic programming problem that minimizes intra-class distance while preserving interclass relationship in the original raw image feature space. Query-adaptive weights are then rapidly computed by evaluating the proximity between a query and the concept categories. With the adaptive bit weights, the returned images can be ordered by weighted Hamming distance at a finer-grained binary code level rather than at the original integer Hamming distance level. Experimental results on a Flickr image dataset show clear improvements from our query-adaptive ranking approach.