Andrés Gago-Alonso

Frequent approximate subgraphs as features for graph-based image classification

The use of approximate graph matching for frequent subgraph mining has been identified in different applications as a need. To meet this need, several algorithms have been developed, but there are applications where it has not been used yet, for example image classification. In this paper, a new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM (Vertex and Edge Approximate graph Miner) is presented. Slight variations of the data, keeping the topology of the graphs, are allowed in this algorithm. Approximate matching in existing algorithm (APGM) is only performed on vertex label set. In VEAM, the approximate matching between edge label set in frequent subgraph mining is included in the mining process. Also, a framework for graph-based image classification is introduced. The approximate method of VEAM was tested on an artificial image collection using a graph-based image representation proposed in this paper. The experimentation on this collection shows that our proposal gets better results than graph-based image classification using some algorithms reported in related work.

Fingerprint indexing with bad quality areas

Fingerprint indexing is a key technique in fingerprint identification systems. This strategy allows us to reduce the search space and the occurrences of false acceptance in databases with great size. This paper presents a new triplet based indexing algorithm which uses a new fingerprint representation, based on minutia triplets. This representation is an extension of the triangle set obtained from Delaunay triangulation. Also, a strategy is proposed in order to dismiss bad quality triplets that could affect the accuracy of the indexing process. This proposal shows a good accuracy, even when the fingerprints have bad quality areas.

A new proposal for graph-based image classification using frequent approximate subgraphs

Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. In computer vision, graphs have been used to model images in order to add some high level information (relations) to the low-level representation of individual parts. How to deal with these representations for specific tasks is not easy due to the complexity of the data structure itself. In this paper we propose to use a graph mining technique for image classification, introducing approximate patterns discovery in the mining process in order to allow certain distortions in the data being modeled. We are proposing to combine a powerful graph-based image representation adapted to this specific task and frequent approximate subgraph (FAS) mining algorithms in order to classify images. In the case of image representation we are proposing to use more robust descriptors than our previous approach in this topic, and we also suggest a criterion to select the isomorphism threshold for the graph mining step. This proposal is tested in two well-known collections to show the improvement with respect to the previous related works. HighlightsWe propose a new framework for image classification, which uses frequent approximate subgraph patterns as features.We propose to compute automatically the substitution matrices needed in the process, instead of using expert knowledge.We propose to use a new graph-based image representation.We propose a criterion for selecting isomorphism threshold for the graph mining process.

Full duplicate candidate pruning for frequent connected subgraph mining

Support calculation and duplicate detection are the most challenging and unavoidable subtasks in frequent connected subgraph (FCS) mining. The most successful FCS mining algorithms have focused on optimizing these subtasks since the existing solutions for both subtasks have high computational complexity. In this paper, we propose two novel properties that allow removing all duplicate candidates before support calculation. Besides, we introduce a fast support calculation strategy based on embedding structures. Both properties and the new embedding structure are used for designing two new algorithms: gdFil for mining all FCSs; and gdClosed for mining all closed FCSs. The experimental results show that our proposed algorithms get the best performance in comparison with other well known algorithms.

A new proposal for graph classification using frequent geometric subgraphs

Geometric graph mining has been identified as a need in many applications. This technique detects recurrent patterns in data taking into account some geometric distortions. To meet this need, some graph miners have been developed for detecting frequent geometric subgraphs. However, there are few works that attend to actually apply this kind of pattern as feature for classification tasks. In this paper, a new geometric graph miner and a framework, for using frequent geometric subgraphs in classification, are proposed. Our solution was tested in the already reported AIDS database. The experimentation shows that our proposal gets better results than graph-based classification using non-geometric graph miners.

On Speeding up Frequent Approximate Subgraph Mining

Frequent approximate subgraph (FAS) mining has become an interesting task with wide applications in several domains of science. Most of the previous studies have been focused on reducing the search space or the number of canonical form (CF) tests. CF-tests are commonly used for duplicate detection; however, these tests affect the efficiency of mining process because they have high computational complexity. In this paper, two prunes are proposed, which allow decreasing the label space, the number of candidates and the number of CF-tests. The proposed prunes are already used and validated in two reported FAS miners by speeding up their mining processes in artificial graph collections.

Extension of Canonical Adjacency Matrices for Frequent Approximate Subgraph Mining on Multi-Graph Collections

Into the data mining field, frequent approximate subgraph (FAS) mining has become an important technique with a broad spectrum of real-life applications. This fact is because several real-life phenomena can be modeled by graphs. In the literature, several algorithms have been reported for mining frequent approximate patterns on simple-graph collections; however, there are applications where more complex data structures, as multi-graphs, are needed for modeling the problem. But to the best of our knowledge, there is no FAS mining algorithm designed for dealing with multi-graphs. Therefore, in this paper, a canonical form (CF) for simple-graphs is extended to allow representing multi-graphs and a state-of-the-art algorithm for FAS mining is also extended for processing multi-graph collections by using the extended CF. Our experiments over different synthetic and real-world multi-graph collections show that the proposed algorithm has a good performance in terms of runtime and scalability. Additionally, we show the usefulness of the patterns computed by our algorithm in an image classification problem where images are represented as multi-graphs.

A New Method Based on Graph Transformation for FAS Mining in Multi-graph Collections

Currently, there has been an increase in the use of frequent approximate subgraph FAS mining for different applications like graph classification. In graph classification tasks, FAS mining algorithms over graph collections have achieved good results, specially those algorithms that allow distortions between labels, keeping the graph topology. However, there are some applications where multi-graphs are used for data representation, but FAS miners have been designed to work only with simple-graphs. Therefore, in this paper, in order to deal with multi-graph structures, we propose a method based on graph transformations for FAS mining in multi-graph collections.

Using Reference Point as Feature for Fingerprint Indexing

This work introduces a new feature based on relative minutia position regarding a reference point. The introduction of this feature, allows the elimination of false matches generated by minutiae. Moreover, a novel algorithm for detecting the reference point in fingerprints is introduced. This approach was tested in a manually edited dataset and it proved to be highly tolerant to distorted impressions. Moreover, the new feature was integrated to a recent fingerprint indexing algorithm in an efficient way. Well known fingerprint datasets were employed to show the improvement in accuracy and the superiority of the presented method regarding other proposals.

A new algorithm for mining frequent connected subgraphs based on adjacency matrices

Most of the Frequent Connected Subgraph Mining (FCSM) algorithms have been focused on detecting duplicate candidates using canonical form (CF) tests. CF tests have high computational complexity, which affects the efficiency of graph miners. In this paper, we introduce novel properties of the canonical adjacency matrices for reducing the number of CF tests in FCSM. Based on these properties, a new algorithm for frequent connected subgraph mining called grCAM is proposed. The experiments on real world datasets show the impact of the proposed properties in FCSM. Besides, the performance of our algorithm is compared against some other reported algorithms.