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Prior studies using graph neural networks (GNNs) for image classificationhave focused on graphs generated from a regular grid of pixels or similar-sizedsuperpixels. In the latter, a single target number of superpixels is definedfor an entire dataset irrespective of differences across images and theirintrinsic multiscale structure. On the contrary, this study investigates imageclassification using graphs generated from an image-specific number ofmultiscale superpixels. We propose WaveMesh, a new wavelet-based superpixelingalgorithm, where the number and sizes of superpixels in an image aresystematically computed based on its content. WaveMesh superpixel graphs arestructurally different from similar-sized superpixel graphs. We use SplineCNN,a state-of-the-art network for image graph classification, to compare WaveMeshand similar-sized superpixels. Using SplineCNN, we perform extensiveexperiments on three benchmark datasets under three local-pooling settings: 1)no pooling, 2) GraclusPool, and 3) WavePool, a novel spatially heterogeneouspooling scheme tailored to WaveMesh superpixels. Our experiments demonstratethat SplineCNN learns from multiscale WaveMesh superpixels on-par withsimilar-sized superpixels. In all WaveMesh experiments, GraclusPool performspoorer than no pooling / WavePool, indicating that poor choice of pooling canresult in inferior performance while learning from multiscale superpixels.

Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels