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Person re-identification aims to match people across non-overlapping camera views, which is an important and challenging task. In order to obtain a robust metric for measuring (dis)similarities of (un)matched image pairs, metric learning has been introduced recently. Most existing works focus on seeking a Mahalanobis distance by employing sparse pair wise (dis)similarity constraints. However, the...
Protein-Protein Interaction (PPI) extraction from biomedicine literature can supply the biomedicine researcher with useful information rapidly. This paper presents a PPI extraction system based on the ensemble kernel model and active learning. Firstly, the ensemble kernel within SVM classifier combines the lexical feature-based kernel and the path-based kernel. Experimental results show that the F-score...
For classification tasks, it is always desirable to extract features that are most effective for preserving class separability. In this brief, we propose a new feature extraction method called regularized deep Fisher mapping (RDFM), which learns an explicit mapping from the sample space to the feature space using a deep neural network to enhance the separability of features according to the Fisher...
An important, yet under-explored, problem in pattern recognition concerns learning from data labeled at varying levels of specificity. The majority of existing machine learning methods are based on the inductive learning paradigm, where a labeled training set (one label per training example) trains a classifier which is markedly different from the human learning experience, where any one object can...
The use of an ensemble of feature spaces trained with distance metric learning methods has been empirically shown to be useful for the task of automatically designing local image descriptors. In this paper, we present a quantitative analysis which shows that in general, nonlinear distance metric learning methods provide better results than linear methods for automatically designing local image descriptors...
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