Distance Learning for Similarity Estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneoussourcesthatmayhavedifferent influenceonsimilarityestimation.Therefore,the assumption ofsingleisotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures, which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion track...

• Type: Journal
• Year: 2008
• Where: PAMI
• Authors: Jie Yu, Jaume Amores, Nicu Sebe, Petia Radeva, Qi Tian
• Keywords: Better Distance Measure, Distance Measures, PAMI 2008, Similarity Estimation