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Scene Image Classification Based on Super Pixel Segmentation and Correlated Topic Model

机译：基于超像素分割和相关主题模型的场景图像分类

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摘要

Background: Scene image classification is a fundamental problem in the field of computer vision, as described in various patents. But, so far, it is still a challenging task to solve the semantic gap of the scene image between low level feature and high level topic. Method: In this paper, we propose a new scene image classification method based on super pixel segmentation and correlated topic model. The method is composed of the following steps: Firstly, considering super pixel providing the spatial support for computing region, we divide image into sub-regions through super pixel segmentation model. Then, each sub-region is described by lots of local scale invariant feature transform key points. In order to preserve the mode information of key points, we use Median-shift method to build word of bag to represent image. Lastly, in order to reflect the relation of the low level features and the high topics of images, we use a correlated topic model based on word of bag to classify scene image. Result: We evaluated the proposed method on the classical Caltech 10 database. The experiment results show that the presented method have average precision rate with 72.6% for scene image classification. Conclusion: From the experimental results we can draw the conclusion that the super pixel segmentation method can preserve more spatial support to scene image, and the correlated topic model can mine the high-level semantic information scene categories from low-level feature, which make the presented method highly completive than other approaches.
机译：背景：场景图像分类是计算机视野领域的基本问题，如各种专利中所述。但是，到目前为止，解决低级特征与高级主题之间的场景图像的语义差异仍然是一个具有挑战性的任务。方法：在本文中，我们提出了一种基于超像素分割和相关主题模型的新场景图像分类方法。该方法由以下步骤组成：首先，考虑到提供用于计算区域的空间支持的超像素，我们通过超像素分割模型将图像分成子区域。然后，每个子区域由大量的本地规模不变特征转换关键点。为了保留关键点的模式信息，我们使用中位移方法构建袋子的字表示图像。最后，为了反映低级功能和图像的高主题的关系，我们使用基于包的相关主题模型来分类场景图像。结果：我们在经典CALTECH 10数据库中评估了所提出的方法。实验结果表明，呈现的方法具有平均精度率，对于场景图像分类，具有72.6％。结论：从实验结果我们可以得出结论：超像素分割方法可以保持更多的空间支持，以及相关的主题模型可以从低级功能挖掘高电平语义信息场景类别，这使得呈现的方法比其他方法高度完全。

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