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In this paper, we propose a stand-alone mobile visual search system based onbinary features and the bag-of-visual words framework. The contribution of thisstudy is three-fold: (1) We propose an adaptive substring extraction methodthat adaptively extracts informative bits from the original binary vector andstores them in the inverted index. These substrings are used to refine visualword-based matching. (2) A modified local NBNN scoring method is proposed inthe context of image retrieval, which considers the density of binary featuresin scoring each feature matching. (3) In order to suppress false positives, weintroduce a convexity check step that imposes a convexity constraint on theconfiguration of a transformed reference image. The proposed system improvesretrieval accuracy by 11% compared with a conventional method withoutincreasing the database size. Furthermore, our system with the convexity checkdoes not lead to false positive results.

Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives