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 multichannel GCN that learns features of the most representative image patches. We have investigated both spectral and nonspectral implementations of the GCN node-embedding. Specifically, we have implemented flexgrid2vec based on different nodeaggregation 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.
@article{hamdi2020flexgrid2vec,
title={flexgrid2vec: Learning Efficient Visual Representations Vectors},
author={Hamdi, Ali and Kim, Du Yong and Salim, Flora D},
journal={arXiv preprint arXiv:2007.15444},
year={2020}
}
© 2021 Flora Salim - CRUISE Research Group.