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.