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Combining multiple spatial hidden Markov models in image semantic classification and annotation

机译:在图像语义分类和注释中结合多个空间隐马尔可夫模型

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The spatial-hidden Markov model (SHMM) [11] is a two dimensional generalization of the traditional hidden Markov model (HMM), with the capability of blockbased semantic annotation as well as classification of images. In this paper, we conduct a sensitivity analysis of SHMM in semantic classification with respect to different block sizes and from this analysis, we propose a novel multi-scales SHMM that combines multiple SHMMs, each classifying the image on a different scale. By regarding each SHMM as distinct classifiers, classifier combination algorithm can be applied to integrate the outputs of the respective SHMMs to improve image classification accuracy. Experiment results demonstrate that the multi-scale SHMM consistently outperforms single SHMMin image semantic classifications. The proposed approach can be extended to other block-based image classification algorithms.
机译:空间隐藏的马尔可夫模型(SHMM)[11]是传统隐马尔可夫模型(HMM)的二维泛化,具有块基的语义注释的能力以及图像的分类。在本文中,我们对不同块大小和来自该分析的语义分类中SHMM对SHMM的敏感性分析,我们提出了一种组合多个SHMM的新型多标度SHMM,每个SHMM在不同的尺度上对图像进行分类。关于每个SHMM作为不同的分类器,可以应用分类器组合算法来集成各个SHMM的输出以提高图像分类精度。实验结果表明,多尺度SHMM始终如一地优于单个SHMMIN图像语义分类。所提出的方法可以扩展到其他基于块的图像分类算法。

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