Local image features can be assumed to be drawn from an unknown distribution.For image classification,such features can be compared through the histogram-based model or the metric-based model.By quantizing these local features into a set of histogram,the histogram-based model is convenient and has vectorial representation of image yet information could be lost in vector quantization.Unlike the histogram-based model,the metric-based model estimates the metric over the underlying distribution of local features directly,achieving better predictive performance.However,the model requires higher computational cost and has no vectorial representation of image.To retain the advantages of these two models,this paper proposes the(doubly)randomized distribution features that represent the underlying distribution of local features in each image as a vectorial feature by utilizing random Fourier feature.Remarkable advantages of the randomized distribution feature are that it has vectorial representation and computes efficiently like the histogram-based model.Besides,it has nice theory guarantee and competitive performance as the metricbased model.Comparing with several state-of-the-art algorithms,experiments in object classification dataset justify that our proposed approaches attain competitive classification accuracy with faster computational speed.