Image Classification Based on Data Gravitation

We put forward a method for image classification based on data gravitation. The quality of the data particles we use is the feature of the images(such as the fractal dimension of the image). For each kind of training data particle set, we use the mean of several image characteristics m_i in the set to be the image characteristic, and the number of images w_i to be its weight. So the quality of the i-thtraining data particle set is w_i m_i, and the data particle for inspection is atomic data particle with the mass of 1. Assuming that the characteristic of the image data particle for inspection j is t_mj, the distance between the i-th training data particle set and the data particles for inspection j of the image is |m_i-t_mj|. Assuming that there are three different catagories of images, we choose the part of images from all kinds in order to compose three kinds of training data particle set, then work out the characteristic mean of data particle set of each kind and characteristic value of a data particle for inspection, which can be used to calculate the gravitation between data particle set of each kind and characteristic value of data particle for inspection. The kind with largest gravitation is the one of data particle for inspection. It is proved by the experimental result that the method for image classification based on data gravitation has a certain advantage.