Trademark Image Retrieval Using Inverse Total Feature Frequency and Multiple Detectors

Conventional similar trademark search methods have mainly handled only binary images and measured similarities globally between trademark images. Recent image retrieval methods using the bag-of-visual-words strategy can deal with the same object detection on the some various conditions like image size variation but cannot well handle vague similarity for simple shape objects in particular. However the real task for screening trademark images demands several image retrieval functions such as simultaneous validation of global and local similarities. In this paper we describe more effective methods for managing trademark image screening. Our method is twofold; One is a combination of multiple detectors for more various shape description and the other is an inverse total feature frequency that reflects extracted feature number for weighting each visual word more effectively in the bag-of-visual words strategy. Experiments with real trademark images show that our proposed method achieves higher accuracies than conventional methods.