Adaptive biogeography based optimizer evolution depth convolutional neural network for image classification

Xiang Nan; Rajamohan Parthasarathy

doi:10.1117/12.3005915

10 October 2023 Adaptive biogeography based optimizer evolution depth convolutional neural network for image classification

Xiang Nan, Rajamohan Parthasarathy

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993E (2023) https://doi.org/10.1117/12.3005915
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

How to extract and analyze feature information from significantly different image data, and achieve precise positioning and classification of massive images, is the core issue of image classification research. The dataset in this article contained 10 different types of images, each with approximately 5000 images, with image sizes of 100 × 100, 200 × 200, and 300 × 300. In order to deal with large-scale data, this paper used adaptive biogeographical optimizer and traditional gradient descent methods to optimize multi-layer depth Convolutional neural network (CNN), and designed single-layer adaptive biogeographical network, multi-layer adaptive biogeographical network, single-layer gradient descent network and multilayer gradient descent network for experiments. All classifiers used the softmax function for classification. The accuracy of multi-layer network image classification under the algorithm in this article was 96.6%. The accuracy and efficiency of image classification can be improved by using Biogeography optimization algorithm to optimize the depth CNN.

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

Citation Download Citation

Xiang Nan and Rajamohan Parthasarathy "Adaptive biogeography based optimizer evolution depth convolutional neural network for image classification", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993E (10 October 2023); https://doi.org/10.1117/12.3005915

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