We present a novel and robust method for localizing and segmenting bilaterally symmetric patterns from real-world images. On the basis of symmetrically matched pairs of local features, our method expands and merges condent local symmetric region matches by exploiting both photometric similarity and geometric consistency via our new symmetry-growing framework. It overcomes the limitations of the previous local-feature based approaches by efciently exploring the image space to grow symmetry beyond the detected symmetric features. The experimental evaluation demonstrates that our method successfully detects and segments multiple symmetric patterns from real-world images, and clearly outperforms the state-of-the-art methods in accuracy and robustness.