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Feature Map Upscaling to Improve Scale Invariance in Convolutional Neural Networks

Efforts made by computer scientists to model the visual system has resulted in various techniques from which the most notable has been the Convolutional Neural Network (CNN). Whilst the ability to recognise an object in various scales is a trivial task for the human visual system, it remains a challenge for CNNs to achieve the same behaviour. Recent physiological studies reveal the visual system uses global-first response strategy in its recognition function, that is the visual system processes a wider area from a scene for its recognition function. This theory provides the potential for using global features to solve transformation invariance problems in CNNs. In this paper, we use this theory to propose a feature extraction model called Stacked global-first Filter CNN (SFCNN) to improve scale-invariant classification of images. In SFCNN, to extract features from spatially larger areas of the target image, we develop a trainable feature extraction layer called Stacked Filter Convolutions (SFC). We achieve this by creating a convolution layer with a pyramid of stacked filters of different sizes. When convolved with an input image the outputs are feature maps of different scales which are then and used as global features. Our results show that by integrating the SFC layer within a CNN upsampled structure, the network outperforms traditional CNN on classification of scaled color images. Experiments using benchmark datasets indicate potential effectiveness of our model towards improving scale invariance in