Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Aiming at determining the category of an image captured from mobile robots for intelligent application, classification with the bag-of-words model is proved effectively in near-duplicate/planar images. When it comes to images from mobile robots with complex background, does it still work well? In this paper, based on the merging criterion improvement, a method named hierarchical selective search is proposed hierarchically extracting complementary features to form a combined and environment-adaptable similarity measurement for segmentation resulting a small and high-quality regions set. Simultaneously those regions rather than a whole image are used for classification. As a result, it well improved the classification accuracy and make the bog-of-word model still work well on classification for mobile robots. The experiments on hierarchical selective search show its better performance than selective search on two task datasets for mobile robots. The experiments on classification shows the samples from regions are better than those original whole images. The advantage of less quantity and higher quality object regions from hierarchical selective search is more prominent when it comes to those special tasks for mobile robots with scarce data.