The state of the art rigid object recognition algorithms are based on the bag of words model, which represents each image in the database as a sparse vector of visual words. We
propose a new algorithm to select informative features from images in the database. which can save the memory cost when the database is large and reduce the length of the inverted index so it can improve the recognition speed. Experiments show that only using the informative features selected by our algorithm has better recognition performance than the previous methods.