Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D C-string: a new spatial knowledge representation for image database systems
Pattern Recognition
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practice and challenges in trademark image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Layout indexing of trademark images
Proceedings of the 6th ACM international conference on Image and video retrieval
Indexing through laplacian spectra
Computer Vision and Image Understanding
Efficient logo retrieval through hashing shape context descriptors
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Invariant curvature-based Fourier shape descriptors
Journal of Visual Communication and Image Representation
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To evaluate similarity between two images, the layout or configuration of the shapes is an important feature besides geometrical shape similarity. In particular, trademark image retrieval is an application domain where layout similarity is important, and in many cases overlooked. In this paper, we present a graph-based encoding of layout, in which both directional and topological layout information is stored. A Hermitian matrix is associated to each graph, and contains all the information that is present in the graph. The spectra of these Hermitian matrices are used for indexing purposes. By obeying several constraints on the construction of the Hermitian matrices, we can mimic the spectral behaviour of Laplacian matrices, which are proven to be successful representations in retrieval environments. Experiments show the improved representational power of the proposed approach over spectral methods using Laplacian matrices.