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Open AccessArticle

Bag of ARSRG Words (BoAW)

by Mario Manzo 1,*,† and Simone Pellino 2
1
Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, Italy
2
Secondary School Teacher of Computer Science, Mattei Istitute of Aversa, 81031 Aversa (CE), Italy
*
Author to whom correspondence should be addressed.
Current address: Via Nuova Marina, 59, 80133 Naples, Italy.
Mach. Learn. Knowl. Extr. 2019, 1(3), 871-882; https://doi.org/10.3390/make1030050
Received: 24 June 2019 / Revised: 30 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
(This article belongs to the Section Learning)
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. View Full-Text
Keywords: image classification; object recognition; graph based image representation; space reduction image classification; object recognition; graph based image representation; space reduction
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Manzo, M.; Pellino, S. Bag of ARSRG Words (BoAW). Mach. Learn. Knowl. Extr. 2019, 1, 871-882.

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