Random sampling image to class distance for photo annotation

Image classification or annotation is proved difficult for the computer algorithms. The Naive-Bayes Nearest Neighbor method is pro- posed to tackle the problem, and achieved the state of the art results on Caltech-101 and Caltech-256 image databases. Although the method is simple and fast, for the real applications, it super from the imbalance of the training datasets. In this paper, we extend the image to class dis- tance which is more general, and use the random sampling technique to alleviate the situation of the imbalance of the training datasets. We per- form our method on the ImageCLEF 2010 Photo Annotation task, and the results(INSUNHIT) showing that the algorithm is fast and stable. Although it does not achieving the state of the art performance, more image features can be used to improve the performance and dimension reduction techniques can be adopted to reduce the complexity of space and time.