Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. In this paper, instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which each filter is a linear combination of a small set of base filters learned from data. This added flexibility allows a large number of sample-dependent patterns to be captured, which is especially useful in the handling of large or high-dimensional data sets. Computationally, the resultant model can be efficiently learned by online learning. Extensive experimental results on a number of data sets show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space complexities.