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Feature Map Upscaling to Improve Scale Invariance in Convolutional Neural Networks

Kumar, Dinesh and Sharma., Dharmendra (2021) Feature Map Upscaling to Improve Scale Invariance in Convolutional Neural Networks. [Conference Proceedings]

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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 global-first feature extraction model called Stacked 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 Convolut ions (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 upsampled and used as global features. Our results show that by integrating the SFC layer within a CNN 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 CNN networks.

Item Type: Conference Proceedings
Additional Information: umar, D. and Sharma, D. (2021). Feature Map Upscaling to Improve Scale Invariance in Convolutional Neural Networks.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-488-6, pages 113-122. DOI: 10.5220/0010246001130122 ; 16th International Conference on Computer Vision Theory and Applications, VISAPP 2021 ; Conference date: 08-02-2021 Through 10-02-2021
Uncontrolled Keywords: ConvolutionalNeuralNetwork, FeatureMap, FilterPyramid, GlobalFeature, ScaleInvariance, VisualSystem
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User: Dinesh Kumar
Date Deposited: 04 Mar 2021 00:44
Last Modified: 04 Mar 2021 00:44

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