To improve the universality of image feature extraction, an image feature extraction method based on improved Nonnegative Matrix Factorization (NMF) was proposed. Firstly, considering the practical significance of extracted image features, NMF model was used to reduce the dimension of image feature vector. Secondly, in order to represent the image by a small number of coefficients, a sparse constraint was added to the NMF model as one of the regular terms. Then, to make the optimized feature have better inter-class differentiation, the clustering property constraint would be another regular term of the NMF model. Finally, through optimizing the model by using gradient descent method, the best feature basis vector and image feature vector could be acquired. The experimental results show that for three image databases, the acquired features extracted by the improved NMF model are more conducive to correct image classification or identification, and the False Accept Rate (FAR) and False Reject Rate (FRR) are reduced to 0.021 and 0.025 respectively.