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Apart from the existing temporal and inter-view interpolation technique in distributed multi-view video coding (DMVC), this paper is dedicated to not only investigating multiple side information implication at the decoder, but also preserving the constrained relaxation with high-level features matching. We present a novel feature-based Wyner-Ziv coding framework (FWZC) for DMVC, which devotes scale-invariant features extraction and matching to generate inter-view correlated side information without knowing the camera parameters and has a more significant improvement rate-distortion performance. The scale-invariant local features are identified as the most resistant to image deformations and affine distortion between different views of an object or scene. The plausible filling-in from a priori distinctive feature constraints between neighboring views could make a promising compensation to inter-view side information generation for joint multi-view decoding. The experimental results show high precision for objects with high motion and quite significant improvement in the rate-distortion performance.