Local Semantic Classification of Natural Image Based on Spatial Context

Image classification is a challenging research topic in image analyzing, and it is widely used in the area of image labelling and image semantic retrieval. In this paper, we first define a set of local semantic concepts to describe the local scene content, and then use the AdaBoost classifier to recognize the local semantics of the natural scene images. Furthermore, we propose three rules based on spatial context, which are semantic filtering, horizontal boundary and relative position of Sky, so as to improve the recognition accuracy of the local image semantic. Experiment result shows the effectiveness of our model.