Cross-modal hashing is a successful method to solve large-scale multimedia retrieval issue. A lot of matrix factorization-based hashing methods are proposed. However, the existing methods still struggle with a few problems, such as how to generate the binary codes efficiently rather than directly relax them to continuity. In addition, most of the existing methods choose to use an $n\times n$ similarity matrix for optimization, which makes the memory and computation unaffordable. In this paper we propose a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. It firstly introduces a collective matrix factorization to learn a common latent space from the kernelized features of different modalities, and then transforms the similarity matrix optimization to a distance-distance difference problem minimization with the help of semantic labels and common latent space. Hence, the computational complexity of the $n\times n$ asymmetric optimization is relieved. In the generation of hash codes we also employ an orthogonal constraint of label information, which is indispensable for search accuracy. So the redundancy of computation can be much reduced. For efficient optimization and scalable to large-scale datasets, we adopt the two-step approach rather than optimizing simultaneously. Extensive experiments on three benchmark datasets: Wiki, MIRFlickr-25K, and NUS-WIDE, demonstrate that our ASCMH outperforms the state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
翻译:跨模式的散列是解决大型多媒体检索问题的成功方法。 提出了大量基于信息基数的分量散列方法。 但是, 现有的方法仍然与几个问题挣扎, 比如如何高效生成二进制代码, 而不是直接将二进制代码放松到连续性。 此外, 大多数现有方法选择使用 $n\ timen n$ 相似的矩阵来优化, 这使得内存和计算 $ n$ 不对称优化的计算复杂性变得不可承受。 在生成新颖的 Asssymation 可缩放跨模式( ASCMH ) 来解决这些问题。 它首先引入了集体矩阵化, 从不同模式的内嵌特性中学习共同的潜在空间, 然后将相似的矩阵优化转化为远程差异最小化 。 因此, $n\ time timen n n$ 相近度优化的计算复杂性可以减轻。 在生成有线的代码中, 我们还使用一个或多调制的标签信息限制, 这对于搜索准确性是不可或缺的。 因此, ASMRM 的重缩缩缩缩缩算方法可以同时使用。