In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification. CorrLog can be efficiently learned, though approximately, by regularized maximum pseudo likelihood estimation, and it enjoys a satisfying generalization bound that is independent of the number of labels. CorrLog performs competitively for multilabel image classification on benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL VOC 2012, compared with the state-of-the-art multilabel classification algorithms.
翻译:CorrLog将常规后勤回归模型扩展为多标签案例,通过对标签之间的对等关系进行明确的建模;此外,我们提议学习具有弹性网络正规化的CorrLog模型参数,这有助于利用地物选择和标签相关性的宽度,从而进一步提高多标签分类的性能。CorrLog可以通过定期化的最大假可能性估计来有效学习,并享有与标签数量无关的令人满意的概括性约束。CorrLog在基准数据集MULAN现场、MIT室外场景、PASCAL VOC 2007和PASCAL VOC 2012的基准数据集多标签图像分类方面竞争,与最先进的多标签分类算法相比。