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

Morales-González, Annette; Acosta-Mendoza, Niusvel; Gago-Alonso, Andrés; García-Reyes, Edel; Medina-Pagola, José E.

doi:10.1016/j.patcog.2013.07.004

Cited by 24 publications

(20 citation statements)

References 34 publications

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“…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

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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.

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Section: Methodsmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

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“…However, many important applications can be tackled by representing patterns as ''non-geometric'' entities. For instance, it is possible to cite applications in document analysis (Bunke and Riesen 2011), solubility of E. coli proteome , bio-molecules recognition (Ceroni et al 2007;Rupp and Schneider 2010), chemical structures generation (White and Wilson 2010), image analysis (Serratosa et al 2013;Morales-González et al 2014), and scene understanding (Brun et al 2014). The availability of interesting datasets containing non-geometric data motivated the development of pattern recognition and soft computing techniques on such domains Rossi et al 2015;Fischer et al 2015;Lange et al 2015;Schleif 2014;Bianchi et al 2015).…”

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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“…Table 4 shows results on the ETH-80 dataset. The same setup reported in [18] to perform a direct comparison is adopted. The setting consists of six categories (apples, cars, cows, cups, horses, and tomatoes).…”

Section: Methodsmentioning

confidence: 99%

A Novel Graph Embedding Framework for Object Recognition

Manzo

Pellino

Petrosino

et al. 2015

Lecture Notes in Computer Science

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In recent years research has been producing an important effort to encode the digital image content. Most of the adopted paradigms only focus on local features and lack in information about location and relationships between them. To fill this gap, we propose a framework built on three cornerstones. First, ARSRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) regions graph), for image representation, is adopted. Second, a graph embedding model, with purpose to work in a simplified vector space, is applied. Finally, Fast Graph Convolutional Networks perform classification phase on a graph based dataset representation. The framework is evaluated on state of art object recognition datasets through a wide experimental phase and is compared with well-known competitors.

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

Cited by 24 publications

References 34 publications

Bag of ARSRG Words (BoAW)

Bag of ARSRG Words (BoAW)

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

A Novel Graph Embedding Framework for Object Recognition

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