Frequent approximate subgraphs as features for graph-based image classification

Acosta-Mendoza, Niusvel; Gago-Alonso, Andrés; Medina-Pagola, José E.

doi:10.1016/j.knosys.2011.12.002

Cited by 63 publications

(51 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to obtain a valid comparison with the methods in [26,27] we adopted the same settings: 25 objects are randomly selected and 11% are used as the training set and 89% are used as the testing set. Therefore, results obtained by BoVW are shown and those obtained in [26,27] by applying their solution (VFSR) and the approaches proposed in [28] (gdFil), in [29] (APGM), in [30] (VEAM), in [31] (DTROD-AdaBoost), in [32] (RSW+Boosting), in [33] (Sequential Patterns), in [34] (LAF) and in [25] (ARSRGemb). Results are listed in form of average accuracy and the approach that provided the best performance is highlighted.…”

Section: Methodsmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

View full text Add to dashboard Cite

In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.

show abstract

Section: Methodsmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

View full text Add to dashboard Cite

show abstract

“…Vertex and Edge Approximate graph Miner (VEAM) is an algorithm proposed by [13], which is used for frequent approximate subgraph mining. They have introduced a framework where frequent subgraph act as features.…”

Section: Algorithms and Applications Of Graph Isomorphismmentioning

confidence: 99%

A Comparative Study of Graph Isomorphism Applications

Somkunwar¹,

Vaze²

2017

IJCA

View full text Add to dashboard Cite

Graphs are powerful data structure to represent objects and their concepts. Objects are nothing but nodes and edges describes relation among objects. Number of real world problems is represented by graph. Graph isomorphism is the area of pattern matching and widely used in various applications such as image processing, protein structure, computer and information system, chemical bond structure, Social Networks. This paper surveys both various applications of graph isomorphism and their importance in the society.

show abstract

“…With the purpose of constructing substitution matrix [34], we need to normalize these non-positive values by simply mapping them to zeros in substitution matrix Sn. Specifically, entries of Sn with negative values are defined as zeros, otherwise, they are equal to the correspondingĨ ij .…”

Section: Approximate Kernel Matchingmentioning

confidence: 99%

Using Quadtree-Based Semantic Link Network for Image Classification

Shi

Huang

2013

2013 Ninth International Conference on Semantics, Knowledge and Grids

View full text Add to dashboard Cite

Bag-of-words model is widely utilized for representing images in a semantic intermediate manner. However traditional visual words are orderless without information with regard to co-occurrences of them as well as their spatial distributions. Spatial pyramid matching provides an effective way to preserve partly spatial information within images, but ignores geometric relations between visual words. By means of quadrant division, this paper proposed a novel methodology called quadtree-based Semantic Link Network(qt-SLN) to represent images in the form of semantic network to keep the structural and topological information of an image. The task of image classification then becomes one of classifying graphs which can be implemented with graph kernels defined on different structured data. In addition, co-occurrences of visual words are also modeled using Pointwise Mutual Information(PMI), which is exploited as a substitution matrix in approximate graph matching after normalization. The experimental results show that incorporating structural relations and co-occurrences of visual words allows for a more semantical framework of classification task.

show abstract

Frequent approximate subgraphs as features for graph-based image classification

Cited by 63 publications

References 18 publications

Bag of ARSRG Words (BoAW)

Bag of ARSRG Words (BoAW)

A Comparative Study of Graph Isomorphism Applications

Using Quadtree-Based Semantic Link Network for Image Classification

Contact Info

Product

Resources

About