Niusvel Acosta-Mendoza

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.

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.

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.

Feature Space Reduction for Graph-Based Image Classification

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining FASM is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs FAS leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features.

Image Classification Using Frequent Approximate Subgraphs

Frequent approximate subgraph (FAS) mining is used in applications where it is important to take into account some tolerance under slight distortions in the data. Following this consideration, some FAS miners have been developed and applied in several domains of science. However, there are few works related to the application of these types of graph miners in classification tasks. In this paper, we propose a new framework for image classification, which uses FAS patterns as features. We also propose to compute automatically the substitution matrices needed in the process, instead of using expert knowledge. Our approach is tested in two real image collections showing that it obtains good results, comparable to other non-miner solutions reported, and that FAS mining is better than the exact approach for this task.

Improving graph-based image classification by using emerging patterns as attributes

In recent years, frequent approximate subgraph (FAS) mining has been used for image classification. However, using FASs leads to a high dimensional representation. In order to solve this problem, in this paper, we propose using emerging patterns for reducing the dimensionality of the image representation in this approach. Using our proposal, a dimensionality reduction over 50% of the original patterns is achieved, additionally, better classification results are obtained. HighlightsWe combine FASs together with emerging patterns for image classification.To the best of our knowledge, this is the first work that proposes such combination.A dimensionality reduction of over 50% of the original patterns is achieved.Improvements on classification results are achieved.

Image Clustering Based on Frequent Approximate Subgraph Mining.

Frequent approximate subgraph (FAS) mining and graph clustering are important techniques in Data Mining with great practical relevance. In FAS mining, some approximations in data are allowed for identifying graph patterns, which could be used for solving other pattern recognition tasks like supervised classification and clustering. In this paper, we explore the use of the patterns identified by a FAS mining algorithm on a graph collection for image clustering. Some experiments are performed on image databases for showing that by using the FASs mined from a graph collection under the bag of features image approach, it is possible to improve the clustering results reported by other state-of-the-art methods.