Exploring residual and spatial consistency for object detection

Local image features show a high degree of repeatability, while their local appearance usually does not bring enough discriminative pattern to obtain a reliable matching. In this paper, we present a new object matching algorithm based on a novel robust estimation of residual consensus and flexible spatial consistency filter. We evaluate the similarity between different homography model via two-parameter integratedWeibull distribution and inlier probabilities estimates, which can select uncontaminated model to help eliminating outliers. Spatial consistency test was encoded by the geometric relationships of domain knowledge in two directions, which is invariant to scale, rotation, and translation especially robust to the flipped image. Experiment results on nature images with clutter background demonstrate our method effectiveness and robustness.