Layout indexing of trademark images

  • Authors:

  • Reinier H. van Leuken;M. Fatih Demirci;Victoria J. Hodge;Jim Austin;Remco C. Veltkamp

  • Affiliations:

  • Universiteit Utrecht;Universiteit Utrecht;University of York;University of York;Universiteit Utrecht

  • Venue:
  • Proceedings of the 6th ACM international conference on Image and video retrieval
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Ensuring the uniqueness of trademark images and protecting their identities are the most important objectives for the trademark registration process. To prevent trademark infringement, each new trademark must be compared to a database of existing trademarks. Given a newly designed trademark image, trademark retrieval systems are not only concerned with finding images with similar shapes but also locating images with similar layouts. Performing a linear-search, i.e., computing the similarity between the query and each database entry and selecting the closest one, is inefficient for large database systems. An effective and efficient indexing mechanism is, therefore, essential to select a small collection of candidates. This paper proposes a framework in which a graph-based indexing schema will be applied to facilitate efficient trademark retrieval based on spatial relations between image components, regardless of mutual shape similarity. Our framework starts by segmenting trademark images into distinct shapes using a shape identification algorithm. Identified shapes are then encoded automatically into an attributed graph whose vertices represent shapes and whose edges show spatial relations (both directional and topological) between the shapes. Using a graph-based indexing schema, the topological structure of the graph as well as that of its subgraphs are represented as vectors in which the components correspond to the sorted Laplacian eigenvalues of the graph or subgraphs. Having established the signatures, the indexing amounts to a nearest neighbour search in a model database. For a query graph and a large graph data set, the indexing problem is reformulated as that of fast selection of candidate graphs whose signatures are close to the query signature in the vector space. An extensive set of recognition trials, including a comparison with manually constructed graphs, show the efficacy of both the automatic graph construction process and the indexing schema.