As a new communication paradigm, semantic communication has received widespread attention in communication fields. However, since the decoding of semantic signals relies on contextual knowledge, misalignment between the starting position of the semantic signal and the AI-based semantic decoder would prevent source signal recovery and reconstruction. To achieve more precise semantic communication, this study proposes an image-based semantic synchronization method leveraging intrinsic semantic features of image content. Specifically, a shared synchronized image (SyncImg) is encoded into a synchronization vector header at the transmitter and sent to the receiver. The receiver adopts a sliding window semantic decoder combined with classification and template matching methods to locate the synchronization point. Experimental results demonstrate that compared with traditional methods, the proposed method achieves a lower miss detected ratio (MDR) and root-mean-square error (RMSE) under low signal-to-noise ratios, realizing accurate synchronization of semantic signals across different devices.