Time-efficient Logo Spotting using Text/Non-text Separation as Preprocessing and Approximate Nearest Neighbor Search
Résumé
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 key-
point features to be matched, we propose to utilize a text/non-text separation method to get rid
ABSTRACT.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.