Benoit Gaüzère

Graph edit distance contest: Results and future challenges

Graph Distance Contest (GDC) was organized in the context of ICPR 2016. Its main challenge was to inspect and report performances and effectiveness of exact and approximate graph edit distance methods by comparison with a ground truth. This paper presents the context of this competition, the metrics and datasets used for evaluation, and the results obtained by the eight submitted methods. Results are analyzed and discussed in terms of computation time and accuracy. We also highlight the future challenges in graph edit distance regarding both future methods and evaluation metrics. The contest was supported by the Technical Committee on Graph-Based Representations in Pattern Recognition (TC-15) of the International Association of Pattern Recognition (IAPR).

Approximate Graph Edit Distance by Several Local Searches in Parallel

Solving or approximating the linear sum assignment problem (LSAP) is an important step of several constructive and local search strategies developed to approximate the graph edit distance (GED) of two attributed graphs, or more generally the solution to quadratic assignment problems. Constructive strategies find a first estimation of the GED by solving an LSAP. This estimation is then refined by a local search strategy. While these search strategies depend strongly on the initial assignment, several solutions to the linear problem usually exist. They are not taken into account to get better estimations. All the estimations of the GED based on an LSAP select randomly one solution. This paper explores the insights provided by the use of several solutions to an LSAP, refined in parallel by a local search strategy based on the relaxation of the search space, and conditional gradient descent. Other generators of initial assignments are also considered, approximate solutions to an LSAP and random assignments. Experimental evaluations on several datasets show that the proposed estimation is comparable to more global search strategies in a reduced computational time.

Graph edit distance as a quadratic program

The graph edit distance (GED) measures the amount of distortion needed to transform a graph into another graph. Such a distance, developed in the context of error-tolerant graph matching, is one of the most flexible tool used in structural pattern recognition. However, the computation of the exact GED is NP-complete. Hence several suboptimal solutions, such as the ones based on bipartite assignments with edition, have been proposed. In this paper we propose a binary quadratic programming problem whose global minimum corresponds to the exact GED. This problem is interpreted as a quadratic assignment problem (QAP) where some constraints have been relaxed. This allows to adapt the integer projected fixed point algorithm, initially designed for the QAP, to efficiently compute an approximate GED by finding an interesting local minimum. Experiments show that our method remains quite close to the exact GED for datasets composed of small graphs, while keeping low execution times on datasets composed of larger graphs.

Semantic Web Technologies for Object Tracking and Video Analytics

As demonstrated in several research contexts, some of the best performing state of the art algorithms for object tracking integrate a traditional bottom-up approach with some knowledge of the scene and aims of the algorithm. In this paper, we propose the use of the Semantic Web technology for representing high-level knowledge describing the elements of the scene to be analysed. In particular, we demonstrate how to use the OWL ontology language to describe scene elements and their relationships together with a SPARQL based rule language to infer on the knowledge. The proof of the implemented concept prototype is able to track people even when occlusions between persons and/or objects occur, only using the bounding box dimensions, positions and directions. We also demonstrate how the Semantic Web Technology enables powerful video analytics functions for video surveillance applications.

Approximating Graph Edit Distance Using GNCCP

The graph edit distance (GED) is a flexible and widely used dissimilarity measure between graphs. Computing the GED between two graphs can be performed by solving a quadratic assignment problem (QAP). However, the problem is NP complete hence forbidding the computation of the optimal GED on large graphs. To tackle this drawback, recent heuristics are based on a linear approximation of the initial QAP formulation. In this paper, we propose a method providing a better local minimum of the QAP formulation than our previous proposition based on IPFP. We adapt a convex concave regularization scheme initially designed for graph matching which allows to reach better local minimum and avoids the need of an initialization step. Several experiments demonstrate that our method outperforms previous methods in terms of accuracy, with a time still much lower than the computation of a GED.

Human Tracking Using a Top-Down and Knowledge Based Approach

In this paper, we propose a new top-down and knowledge-based approach to perform human tracking in video sequences. First, introduction of knowledge allows to anticipate most of common problems encountered by tracking methods. Second, we define a top-down approach rather than a classical bottom-up approach to encode the knowledge. The more global point of view of the scene provided by our top-down approach also allows to keep some consistency among the set of trajectories extracted from the video sequence. A preliminary experimentation has been conducted over some challenging sequences of the PETS 2009 dataset. The obtained results confirm that our approach can still achieve promising performance even with a consistent reduction in the amount of information taken into account during the tracking process. In order to show the relevance of considering knowledge to address tracking problem, we strongly reduce the amount of information provided to our approach.

Graph Kernel Encoding Substituents' Relative Positioning

Chemo informatics aims to predict molecular properties using informational methods. Computer sciences research fields concerned by this domain are machine learning and graph theory. An interesting approach consists in using graph kernels which allow to combine graph theory and machine learning frameworks. Graph kernels allow to define a similarity measure between molecular graphs corresponding to a scalar product in some Hilbert space. Most of existing graph kernels proposed in chemo informatics do not allow to explicitly encode cyclic information, hence limiting the efficiency of these approaches. In this paper, we propose to define a cyclic representation encoding the relative positioning of substituents around a cycle. We also propose a graph kernel taking into account this information. This contribution has been tested on three classification problems proposed in chemo informatics.