  We propose flexgrid2vec, a novel approach for image representation learning.
Existing visual representation methods suffer from several issues, including
the need for highly intensive computation, the risk of losing in-depth
structural information and the specificity of the method to certain shapes or
objects. flexgrid2vec converts an image to a low-dimensional feature vector. We
represent each image with a graph of flexible, unique node locations and edge
distances. flexgrid2vec is a multi-channel GCN that learns features of the most
representative image patches. We have investigated both spectral and
non-spectral implementations of the GCN node-embedding. Specifically, we have
implemented flexgrid2vec based on different node-aggregation methods, such as
vector summation, concatenation and normalisation with eigenvector centrality.
We compare the performance of flexgrid2vec with a set of state-of-the-art
visual representation learning models on binary and multi-class image
classification tasks. Although we utilise imbalanced, low-size and
low-resolution datasets, flexgrid2vec shows stable and outstanding results
against well-known base classifiers. flexgrid2vec achieves 96.23% on CIFAR-10,
83.05% on CIFAR-100, 94.50% on STL-10, 98.8% on ASIRRA and 89.69% on the COCO
dataset.
