Attributed Relational Graph Matching Research Articles

An efficient clustering-based retrieval framework for real crime scene footwear marks

As the most common type of evidence at crime scenes, footwear marks are found more often than fingerprints, and yet left largely unused due to lack of efficient and reliable tools. While the central task is stated simply – retrieve the closest matches among a database of known outsole prints – the difficulty is the poor quality of the marks and a very large and increasing number of outsole patterns. Since grouping the database into clusters can dramatically speed-up retrieval, we propose clustering based on recurring outsole patterns. The clustered database is used to retrieve similar prints for a given crime scene mark. Geometric shapes like line segments, circles and ellipses are proposed as features for crime scene marks. Then these features are structurally represented in the form of an attributed relational graph (ARG). Robust ARG matching is achieved with the introduced footwear print distance (FPD), a similarity measure for footwear prints. Sensitivity analysis of FPD is performed to show its robustness. The proposed system is invariant to scale, translation, rotation and insensitive to noise and degradations of the prints. Experiments show that the approach outperforms other state-of-the-art footwear print retrieval systems.

Face recognition using face-ARG matching

In this paper, we propose a novel line feature-based face recognition algorithm. A face is represented by the Face-ARG model, where all the geometric quantities and the structural information are encoded in an Attributed Relational Graph (ARG) structure, then the partial ARG matching is done for matching Face-ARG's. Experimental results demonstrate that the proposed algorithm is quite robust to various facial expression changes, varying illumination conditions and occlusion, even when a single sample per person is given.

Optimal encoding of graph homomorphism energy using fuzzy information aggregation operators

The attributed relational graph matching (ARG) strategy is a well-known approach to object/pattern recognition. In the past for the parallel solution of ARG matching problem, an overall objective function was constructed using linearly weighted information aggregation function and one set of parameter values was chosen for all models by trial-and-error for the parameters in the function. In this paper, the compatibility between every pair of model and scene attributes is interpreted as a fuzzy value and subsequently the nonlinear fuzzy information aggregation operators are used to fuse the information captured in the chosen attributes. To learn the parameters in the fuzzy information aggregation operators, the “learning from samples” strategy is used. The learning of weight parameters is formulated as an optimisation problem and solved using the gradient projection algorithm based learning procedure. The learning procedure implicitly evaluates ambiguity, robustness and discriminatory power of the relational attributes chosen for graph matching and assigns weights appropriately to the chosen attributes. The learning procedure also enables us to compute a distinct set of optimal parameters for every model to reflect the characteristics of the model so that the homomorphic ARG matching problem can be optimally encoded in the energy function for the model. Experimental results are presented to illustrate effectiveness and necessity of the parameter estimation and learning procedures.

Recognition of handprinted Chinese characters by constrained graph matching

A model-based handwritten Chinese character recognition (HCCR) system is proposed. The characters are represented by attributed relational graphs (ARG) using strokes as ARG vertices. A number of vector relational attributes are also used in the representation to improve the performance of the translation and scale invariant and rotation sensitive recognition system. Since the ETL-8 database is very noisy and broken strokes are commonly encountered, a suitable homomorphic energy function is proposed that allows the segments of a broken stroke of a test character to be matched to the corresponding model stroke. The homomorphic ARG matching energy is minimised using the self-organising Hopfield neural networks [1] [Suganthan, P.N., Teoh, E.K., Mital, D.P., A self-organising Hopfield network for attributed relational graph matching, Image and Vision Computing, 13(1) (1995) 61–73]. An effective formulation is introduced to determine the matching score. The formulation does not penalise the matching scores of test characters with broken strokes. Experiments were performed with 100 classes of characters in the ETL-8 database and 98.9% recognition accuracy has been achieved.