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.
In REF a paradigm called Bag of ARSRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) Regions Graph) Words (BoAW) is proposed.
Bag of ARSRG Words (BoAW)
Machine Learning and Knowledge Extraction
Machine Learning and Knowledge Extraction
Computer Science