grid2vec: Learning Efficient Visual Representations via Flexible Grid-Graphs
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We propose grid2vec, a novel approach for image representation learning based on Graph Convolutional Network (GCN). Existing visual representation methods suffer from several issues, such as requiring high-computation, losing in-depth structures, and being restricted to specific objects. grid2vec converts an image to a low-dimensional feature vector. A key component of grid2vec is Flexible Grid-Graphs, a spatially-adaptive method based on the image key-points, as a flexible grid, to generate the graph representation. It represents each image with a graph of unique node locations and edge distances. Nodes, in Flexible Grid-Graphs, describe the most representative patches in the image. We develop a multi-channel Convolutional Neural Network architecture to learn local features of each patch. We implement a hybrid node-embedding method, i.e., having spectral and non-spectral components. It aggregates the products of neighbours' features and node's eigenvector centrality score. We compare the performance of grid2vec with a set of state-of-the-art representation learning and visual recognition models. grid2vec has only 512 features in comparison to a range from VGG16 with 25,090 to NASNet with 487,874. We show the models' superior accuracy in both binary and multi-class image classification. Although we utilise imbalanced, low-size dataset, grid2vec shows stable and superior results against the well-known base classifiers.
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