Abstract
Image classification becomes a popular research area in computer vision due to the increasing development of image indexing and retrieval tasks. This paper proposes a Flower Henry Gas Solubility Optimization-based Deep Convolution Neural Network (FHGSO-based Deep CNN) for image classification. Initially, the input image is pre-processed through the median filter. Then, the segmentation is performed using the Improved Invasive Weed Flower Pollination Optimization (IIWFPO)-based SegNet. IIWFPO is the integration of the Improved invasive weed optimization (IWO) algorithm and Flower Pollination Algorithm (FPA). Finally, image classification is performed using FHGSO-based Deep CNN. The FHGSO algorithm is developed by integrating the FPA and Henry Gas Solubility Optimization (HGSO) algorithm. The performance of the proposed method is analyzed using the Stanford background dataset and compared with the other image classification methods. The proposed model obtained the value of 0.938, 0.955, and 0.907 for testing accuracy, sensitivity, and specificity, respectively.
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The datasets employed in this research study are available in https://www.kaggle.com/balraj98/stanford-background-dataset.
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Deepa, S.N., Rasi, D. FHGSO: Flower Henry gas solubility optimization integrated deep convolutional neural network for image classification. Appl Intell 53, 7278–7297 (2023). https://doi.org/10.1007/s10489-022-03834-4
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DOI: https://doi.org/10.1007/s10489-022-03834-4