Evaluation of Depth Modality in Convolutional Neural Network Classification of RGB-D Images

Varga, Michal; Jadlovský, Ján

doi:10.15546/aeei-2018-0029

Cited by 4 publications

(2 citation statements)

References 11 publications

(21 reference statements)

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“…Inspired by our findings of the significance of the spatial (depth) information during real-life object classification and scene understanding [37], we decided to focus on the classification of 3D images. Generally, these classification methods can be grouped into view-based and volume, or shape-based techniques, depending on the form of data that are present at the input of the classifier.…”

Section: Related Workmentioning

confidence: 99%

Generative Enhancement of 3D Image Classifiers

2020

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.

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Section: Related Workmentioning

confidence: 99%

Generative Enhancement of 3D Image Classifiers

2020

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“…To be usable as an input for 3D convolution-based networks it has to be converted to a voxel grid format. For this purpose our data transformation pipeline has been used [18].…”

Section: Preprocessing and Transformationmentioning

confidence: 99%

Generative Enhancement of 3D Image Classifiers

Varga

Jadlovsky

Jadlovska

2020

Preprint

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the strengths of both non-generative classifiers and generative modeling. Its purpose is to streamline the creation of new classifiers by embedding existing compatible classifiers in a generative network architecture. The demonstration of this process and evaluation of its effects is performed using a 3D convolutional classifier and its generative equivalent - a conditional generative adversarial network classifier. The results show that the generative model achieves greater classification performance, gaining a relative classification accuracy improvement of 7.43%. Improvement of accuracy is also present when compared to a plain convolutional classifier trained on a dataset augmented with examples produced by a trained generator. This suggests there is a desirable knowledge sharing within the hybrid discriminator-classifier network.

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