Bag-of-words (BoW) representation becomes one of the most popular methods for representing image content and has been successfully applied to object categorization.This paper uses the newly proposed statistics of word activation forces (WAFs) to reduce the redundancy in the codebook used in the BoW model.In such a way,the representation of image features is improved.In addition,the authors propose a method using soft inverse document frequency (Sofl-IDF) to optimize BoW based image features.Given visual words and the dataset,each visual word appears in different amount of images and also different times in each particular image.Some of the visual words appear rare in contrary to the frequent ones.The proposed method balances this case.Experiments show encouraging results in scene categorization by the proposed approach.