Content fingerprinting is a powerful solution for media indexing, searching and digital right management, in which the perceptual content of digital media is summarized to a robust and discriminative digest. In this letter, we develop a general paradigm for image fingerprinting by exploiting the capability of sparse coding in capturing the visual characteristics of digital image. Furthermore, the impact of the dictionary for sparse coding on the performance of fingerprinting algorithm is analyzed. Accordingly, the problem of dictionary learning is studied in the context of content fingerprinting by incorporating the robustness and discriminability requirements. Comparative experiments indicate that the proposed work exhibits much higher content identification accuracy than the state-of-the-art ones, and the dictionary learned by the proposed work can substantially improve the performance of fingerprinting algorithm. In addition, our algorithm is highly efficient, and its average fingerprint computation time is less than 0.024s.