Contextual local primitives for binary patent image retrieval

Abstract

Local features and descriptors that perform well in the case of photographic images are often unable to capture the content of binary technical drawings due to their different characteristics. Motivated by this, a new local feature representation, the contextual local primitives, is proposed in this paper. It is based on the detection of the junction and end points, classification of the local primitives to local primitive words and establishment of the geodesic connections of the local primitives. We exploit the granulometric information of the binary patent images to set all the necessary parameters of the involved mathematical morphology operators and window size for the local primitive extraction, which makes the whole framework parameter free. The contextual local primitives and, their spatial areas as a histogram weighting factor are evaluated by performing binary patent image retrieval experiments. It is found that the proposed contextual local primitives perform better than the local primitives only, the SIFT description of the contextual Hessian points, the SIFT description of local primitives and state of the art local content capturing methods. Moreover, an analysis of the approach in the perspective of a general patent image retrieval system reveals of its being efficient in multiple aspects.