上下文场景识别模型的稀疏贝叶斯判别学习方法

Sparse Bayesian discriminative learning method for context-based scene recognition model

在机器人场景识别问题中,将连续场景的相关性通过基于隐马尔可夫模型的上下文模型进行描述,采用不同于传统的使用生成模型方法学习上下文场景识别模型的方式,首先引入稀疏贝叶斯学习机对上下文模型中图像特征的后验概率进行建模,然后通过贝叶斯原理将稀疏贝叶斯模型与隐马尔可夫模型结合,提出一种能够实现上下文场景识别模型的判别学习方法,在真实场景数据库上的实验结果表明,由该方法得到的上下文场景识别系统具有很好的场景识别能力和泛化特性。

For the robotics scene recognition problem, the relationship between continuous scenes can be modeled by a hidden Markov model based context model. Unlike traditionally used generative method to learn this model, a sparse Bayesian learning machine is adopted to model the posterior probabilities of image features. Then by combining the sparse model with the hidden Markov model using Bayes theory, a discriminative learning method of the context scene recognition model is proposed. The experiments on a real scene database show that the obtained scene recognition system possesses good recognition performance and generalization ability.