In this paper, we address the problem of searching for semantically similar images from a large database. We present a compact coding approach, supervised quantization. Our approach simultaneously learns feature selection that linearly transforms the database points into a low-dimensional discriminative subspace, and quantizes the data points in the transformed space. The optimization criterion is that the quantized points not only approximate the transformed points accurately, but also are semantically separable: the points belonging to a class lie in a cluster that is not overlapped with other clusters corresponding to other classes, which is formulated as a classification problem. The experiments on several standard datasets show the superiority of our approach over the state-of-the art supervised hashing and unsupervised quantization algorithms.
翻译:在本文中,我们处理从大数据库中搜索精密相似图像的问题。 我们展示了一种紧凑的编码方法, 受监督的量化。 我们的方法同时学习了将数据库点线性转换为低维歧视子空间的特征选择, 并对转换空间中的数据点进行了量化。 优化的标准是, 量化的点不仅准确接近转换点, 而且还可以分解: 属于某一类的点位于一个组群中, 它不与其他类组群相重叠, 与其他类组组群相重叠, 后者是一个分类问题。 几个标准数据集的实验显示, 我们的方法优于受监督的艺术状态集成和不受监督的四分化算法。