Joint entropy based learning model for image retrieval

Aiming at the problem above, we proposed one joint entropy based learning model which could reduce the number of learning instances through optimizing the distribution of learning instances. Firstly, the learning instances are pre-selected using improved watershed segmentation method. Then, joint entropy model is used for reducing the possibility of double, useless even mistaken instances existence. After that, a database using a large number of images is built up. Sufficient experiments based on the database show the model's superiority that our model not only could reduce the number of learning instances but also could keep the accuracy of retrieval.