Generic Object Recognition by Tree Conditional Random Field Based on Hierarchical Segmentation

Generic object recognition by a computer is strongly required in various fields like robot vision and image retrieval in recent years. Conventional methods use Conditional Random Field (CRF) that recognizes the class of each region using the features extracted from the local regions and the class co-occurrence between the adjoining regions. However, there is a problem that the discriminative ability of the features extracted from local regions is insufficient, and these methods is not robust to the scale variance. To solve this problem, we propose a method that integrates the recognition results in multi-scales by tree conditional random field based on hierarchical segmentation. As a result of the image dataset of 7 classes, the proposed method has improved the recognition rate by 2.2%.