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

Viet Phuong Le (1) , Nibal Nayef (1) , Muriel Visani (1) , Jean-Marc Ogier (1) , de Tran Cao (2)

1 L3I - Laboratoire Informatique, Image et Interaction - EA 2118
2 CICT - College of Information and Communication Technology

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.

Domaines: Traitement du texte et du document; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Viet Phuong Le, Nibal Nayef, Muriel Visani, Jean-Marc Ogier, de Tran Cao. Time-efficient Logo Spotting using Text/Non-text Separation as Preprocessing and Approximate Nearest Neighbor Search. Semaine du Document Numérique et de la Recherche d'Information SDNRI 2016 (CORIA - CIFED), Mar 2016, Toulouse, France. pp.365 - 380. ⟨hal-01319887⟩