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Searching for the most similar matches to high dimensional feature vectors is the most computationally expensive part of many computer vision and document retrieval systems. This work proposes a time-efficient document retrieval system based on logo spotting. The spotting approach is based on feature matching and grouping. In order to reduce the number of keypoint features to be matched, we propose to utilize a text/non-text separation method to get rid of text layer features which are irrelevant to logo matching. The separation method is used as a fast and effective preprocessing step. We further optimize the key-point feature matching step by using an approximate nearest neighbor search algorithms. The overall document retrieval with focused logo retrieval is evaluated on the standard Tobacco-800 database and also our private advertisement magazine database. The results show that the two proposed speed up steps – specially the text separation – reduce the computation time of the system sharply by 75% and 47% on the two databases respectively, while its precision remains unaffected. MOTS-CLÉS : Détection et recherche de logos1, Séparation texte/non-texte2, Système efficace de détection3.

Time-efficient Logo Spotting using Text/Non-text Separation as Preprocessing and Approximate Nearest Neighbor Search