Shape-based retrieval: A case study with trademark image databases

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a method for trademark image database retrieval based on object shape information that would supplement traditional text-based retrieval systems. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals when a query is presented; in the second stage, the candidates from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1100 images. Each retrieval takes a reasonable amount of computation time (similar to 4-5 s on a Sun Space 20 workstation). The topmost image retrieved by the system agrees with that obtained by human subjects, bur there are significant differences between the ranking of the top-10 images retrieved by our system and the ranking of those selected by the human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes. An improved heuristic has been suggested for more accurate retrievals. The proposed scheme matches filled-in query images against filled-in images from the database, thus using only the gross details in the image. Experiments with database images used as query images have shown that matching on the filled-in database extracts more images within the top-20 retrievals that have similar content. We believe that developing an automatic retrieval algorithm which matches human performance is an extremely difficult and challenging task. However, considering the substantial amount of time and effort needed for a manual retrieval from a large image database, an automatic shape-based retrieval technique can significantly simplify the retrieval task. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.