We present a pixel-region-matching approach for fast semi-supervised video object segmentation. Unlike existing methods that exploit pixel-wise similarities between search and query inputs, our method simultaneously extracts pixel-level and region-level information. Specifically, our method proposes a pixel-wise similarity module and a region-based correlation module for video object segmentation. The region-based correlation module learns object prototypes via appearance features that pay more attention to the most relevant target pixels, while the pixel-wise similarity module obtains subtly discriminative information for fine-grained object segmentation. Finally, we integrate these features by using the proposed pixel-region fusion module and then feed them into decoder networks to predict foreground and background. Notably, our method is an end-to-end trainable framework without online fine-tuning and post-processing. We evaluate our method on three widely-used datasets including DAVIS \(_{16}\) , DAVIS \(_{17}\) , and YouTube-VOS. Experimental results clearly demonstrate the effectiveness of the proposed method compared with state-of-the-art methods.