Pasquale Foggia

A (sub)graph isomorphism algorithm for matching large graphs

We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.

Performance evaluation of the VF graph matching algorithm

The paper discusses the performance of a graph matching algorithm tailored for dealing with large graphs in computer vision without using information about the topology of the graphs to be matched. The algorithm, presented in more detail in other papers (and publicly available on the WWW as VF), is now discussed with reference to its computational complexity and memory requirements. The performance analysis is carried out by theoretically characterizing the matching time and the required memory in the best and worst cases. The theoretical analysis is completed by tests on a database of graphs randomly generated. The algorithm is compared with the one proposed by Ullmann (1976): experimental results confirmed the theoretical expectations, highlighting the overall efficiency of the algorithm. Some results obtained by researchers who recently used the algorithm in application domains requiring a massive use of graph matching techniques are finally reported.

GRAPH MATCHING AND LEARNING IN PATTERN RECOGNITION IN THE LAST 10 YEARS

In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the...

Benchmarking HEp-2 Cells Classification Methods

In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.

Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems

Abstract: Recognition systems based on a combination of different experts have been widely investigated in the recent past. General criteria for improving the performance of such systems are based on estimating the reliability associated with the decision of each expert, so as to suitably weight its response in the combination phase. According to the methods proposed to-date, when the expert assigns a sample to a class, the reliability of such a decision is estimated on the basis of the recognition rate obtained by the expert on the chosen class during the training phase. As a consequence, the same reliability value is associated with every decision attributing a sample to a same class, even though it seems reasonable to take into account its dependence on the quality of the specific sample. We propose a method for estimating the reliability of each single recognition act of an expert on the basis of information directly derived from its output. In this way, the reliability value of a decision is more properly estimated, thus allowing a more precise weighting during the combination phase. The definition of the reliability parameters for widely used classification paradigms is discussed, together with the combining rules employing them for weighting the expert opinions. The results obtained by combining four experts in order to recognise handwritten numerals from a standard character database are presented. Comparison with classical combining rules is also reported, and the advantages of the proposed approach outlined.

A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs

A graph g is called a maximum common subgraph of two graphs, g1 and g2, if there exists no other common subgraph of g1 and g2 that has more nodes than g. For the maximum common subgraph problem, exact and inexact algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. In this paper, two exact algorithms for maximum common subgraph detection are described. Moreover a database containing randomly connected pairs of graphs, having a maximum common graph of at least two nodes, is presented, and the performance of the two algorithms is evaluated on this database.

Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs

Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database.

Graph clustering using the weighted minimum common supergraph

Graphs are a powerful and versatile tool useful for representing patterns in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects for clustering similar patterns. In this paper a new structural method, the Weighted Minimum Common Supergraph (WMCS), for representing a cluster of patterns is proposed. Using this method it becomes easy to extract the common information shared in the patterns of a cluster and separate this information from noise and distortions that usually affect graphs representing real objects. Moreover, experimental results show that WMCS is suitable for performing graph clustering.

Pattern recognition in stained HEp-2 cells: Where are we now?

Indirect Immunouorescence (IIF) images are increasingly being used for the diagnosis of autoimmune diseases. However, the analysis of this kind of images has until now reached a comparatively low level of automation, if compared with other medical imaging techniques. The Special Issue on the Analysis and Recognition of Indirect Immunouorescence Images of the Pattern Recognition journal aims at providing a comprehensive evaluation of the state of the art for the staining pattern classication problem, through the adoption of a common experimental protocol and the testing of all the methods on a publicly available dataset. This paper will present both a survey of the articles accepted for the special issue, highlighting their original aspects, and a detailed comparison and discussion of the corresponding experimental results, in order to assess which are the advantages and disadvantages of each approach.

Early experiences in mitotic cells recognition on HEp-2 slides

Indirect immunofluorescence (IIF) imaging is the recommended laboratory technique to detect autoantibodies in patient serum, but it suffers from several issues limiting its reliability and reproducibility. IIF slides are observed by specialists at the fluorescence microscope, reporting fluorescence intensity and staining pattern and looking for mi-totic cells. Indeed, the presence of such cells is a key factor to assess the correctness of slide preparation process and the reported staining pattern. Therefore, the ability to detect mitotic cells is needed to develop a complete computer-aided-diagnosis system in IIF, which can support the specialists from image acquisition up to image classification. Although recent research in IIF has been directed to image acquisition, image segmentation, fluorescence intensity classification and staining pattern recognition, no works presented methods suited to classify such cells. Hence, this paper presents an heterogeneous set of features used to describe the peculiarities of mitotic cells and then tests five classifiers, belonging to different classification paradigms. The approach has been evaluated on an annotated dataset of mitotic cells. The measured performances are promising, achieving a classification accuracy of 86.5 %.