A Novel Architecture for Trademark Image Retrieval Systems

This paper describes the first phase of an ongoing research project aimed at implementing a trademark retrieval system using an associative memory neural network. The novel aspect of the work described in this paper is the presentation of a new integrated methodology for employing multiple interpretations from different analytical levels of images for image retrieval. In achieving this objective, we extract local features as well as features of the closed figures of images. In deriving alternative interpretations of the images, a segment level gestalt grouping method based on a modification of Sarkar and Boyer’s method is used. In designing the search engine of the system we have adopted a novel similarity assessment criteria based on local features as well as features of the closed figures, which may feasibly be implemented using an associative memory neural network to achieve high performance in retrieval.