基于混合mamf的深度卷积神经网络对室内外图像进行场景分类

J. D. Pakhare, M. Uplane
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引用次数: 0

摘要

图像场景分类是当前图像场景分类的主要研究方向,主要是对图像中的目标进行背景定位。在当前场景中,现有的分类器无法提供分类的准确性。因此,利用混合特征和混合技术进行图像场景分类的新方法被称为Mayfly Moth Flame (MAMF)优化算法依赖深度卷积神经网络(MAMF-based Deep CNN)分类器,这对分类精度产生了积极的影响。该算法通过对害虫和球虱的特征进行杂交,使分类器向获得最优分类性能的方向发展。利用基于mamf的深度CNN分类器实现并分析了混合特征对分类的意义。实验分析表明,本文提出的混合特征与基于mamf的深度CNN分类器在SCID2和SUN-397数据集上的准确率分别达到96.7215%和94.8684%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scene Categorization From Indoor-Outdoor Images Using Hybrid MAMF-Based Deep Convolutional Neural Networks
Image scene categorization is the dominant research area, where the localization of the objects along with the background is performed. At the current scenario, existing classifiers fail to provide the accuracy for the classification. Therefore, a novel approach for image scene categorization is performed using the hybrid features and the Hybrid technique named Mayfly Moth Flame (MAMF) optimization algorithm dependent Deep Convolutional Neural Network (MAMF-based Deep CNN) classifier, which positively impacts on the classification accuracy. This algorithm tunes the classifier towards acquiring the optimal classification performance from the classifier and is developed through interbreeding the characteristic features of the vermins and the caddisflies. The significance of the hybrid features for the classification is implemented and analyzed using the MAMF-based deep CNN classifier. The experimental analysis reveals that the proposed Hybrid features with MAMF-based Deep CNN classifier attains highest accuracy of 96.7215 % and 94.8684 % using SCID2 and SUN-397 datasets, respectively.
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