Efficient Retrieval for Large Scale Metric Learning

In this paper, we address the problem of efficient k-NN classification. In particular, in the context of Mahalanobis metric learning. Mahalanobis metric learning recently demonstrated competitive results for a variety of tasks. However, such approaches have two main drawbacks. First, learning metrics requires often to solve complex and thus computationally very expensive optimization problems. Second, as the evaluation time linearly scales with the size of the data k-NN becomes cumbersome for large-scale problems or real-time applications with limited time budget. To overcome these problems, we propose a metric-based hashing strategy, allowing for both, efficient learning and evaluation. In particular, we adopt an efficient metric learning method for local sensitive hashing that recently demonstrated reasonable results for several large-scale benchmarks. In fact, if the intrinsic structure of the data is exploited by the metric in a meaningful way, using hashing we can compact the feature representation still obtaining competitive results. This leads to a drastically reduced evaluation effort. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include standard machine learning datasets as well as the challenging Public Figures Face Database. On the competitive machine learning benchmarks we obtain results comparable to the state-of-the-art Mahalanobis metric learning and hashing approaches. On the face benchmark we clearly outperform the state-of-the-art in Mahalanobis metric learning. In both cases, however, with drastically reduced evaluation effort.