Generic Object Recognition using CRF by Incorporating BoF as Global Features

摘要

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 CRF tends to fall into the local optimal recognition result because it uses only local features and the relation. To solve this problem, we propose a method that recognizes generic objects by incorporating Bag of Features (BoF) as the global feature into CRF. As a result of the experiment to the image dataset of 21 classes, the proposal method has improved the recognition rate by 6.5%. 1.