Query expansion for VHR image detection

In order to detect the objects of interest, many different approaches have been proposed. One kind of popular approaches are based on template matching, which use a template of the object class to match the image at different positions. The matching can be computed using similarity measures such as the correlation coefficient. These approaches, although easy and robust, has the limitation of not containing to much variability of the object class, especially for shape information. Despite the statistical variation in each kind of object, collecting enough training samples is another problem which is time consuming. Inspire by template matching and incremental learning, a new object-oriented object detection methodology for very high resolution remote sensing images is proposed in this paper. We obtain the first initial query results via the bag-of-visual-words method. Then we introduce two query expansion baseline expansion and PAS expansion to obtain a new incremental model for re-query. In the experiment part, we compare and evaluate the performance of our proposed methods.