Research on image features classification based on graph convolutional neural network

Xuetian Xu

doi:10.1117/12.3004260

11 October 2023 Research on image features classification based on graph convolutional neural network

Xuetian Xu

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003Y (2023) https://doi.org/10.1117/12.3004260
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

At present, the image features extraction and classification have widely utilized to detect objects in numerous applications. Additionally, existing classification are primary concentrated on the machine learning models or train a convolutional neural network, which can obtain the acceptable extraction and classification accuracy. However, these methods ignore the graph convolutional operation can also be utilized to achieve the image features and classification tasks. In this work, we initially extend the graph convolutional neural network to the image features extraction and classifications, which can also obtain an acceptable classification accuracy with approximately to 95%. Initially, we utilize the clustering algorithm to mix multiple image pixels to a graph node and the adjacent matrix is depended on the pixel value features. Subsequently, the transferred graph is trained by a graph convolutional neural network to identify the class of these images. From our extensive experimental and compare with traditional machine learning models, our proposed model can successfully identify the subjects in the images with highest classification accuracy and reasonable computation costs.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuetian Xu "Research on image features classification based on graph convolutional neural network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003Y (11 October 2023); https://doi.org/10.1117/12.3004260

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