Joint entropy based learning model for image retrieval

As one classic technique of computer vision, image retrieval could retrieve the target images from hundreds of thousands of images effectively. Furthermore, with the rapid development of deep learning, the quality of retrieval is increased obviously. However, under normal conditions, the high-quality retrieval is supported by a large number of learning instances. The large number of learning instances not only need much human source in the process of selection, but also need much computing source in the process of computation. More importantly, for some special categories, it's difficult to obtain a large number of learning instances. 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.