Laplacian object: One-shot object detection by locality preserving projection

One shot, generic object detection involves detecting a single query image in a target image. Relevant approaches have benefitted from features that typically model the local similarity patterns. Also important is the global matching of local features along the object detection process. In this paper, we consider such global information early in the feature extraction stage by combining local geodesic structure (encoded by LARK descriptors) with a global context (i.e., graph structure) of pairwise affinities among the local descriptors. The result is an embedding of the LARK descriptors (extracted from query image) into a discriminatory subspace (obtained using locality preserving projection [1]) that preserves the local intrinsic geometry of the query image patterns. Experiments on standard data sets demonstrate efficacy of our proposed approach.