A comparative analysis between late fusion of features approach and ensemble of multiple classifiers approach for image classification. (17th May 2021)

• Record Type: Journal Article
• Authors: Choudhury, Khanjan; Murugan, R.; Azharuddin Laskar, Mohammad; Laskar, Rabul Hussain
• Is Part Of: Concurrency and computation. Volume 33:Number 20(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-17
• Subjects: ensemble of multiple classifiers -- high‐level feature -- image classification -- late fusion -- Logsoftmax; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6371
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In recent times the late fusion of features approach for high‐level features extracted by multiple deep convolutional neural networks (DCNNs) has proven to be very effective in the computer vision field, especially for object classification problems. Pretrained DCNNs DenseNet‐121 and ResNet‐18 are retrained, keeping the number of output nodes equal to the number of classes present in the dataset. The last fully connected layers of these networks thereby get adapted to transform the high‐level features to a low‐dimensional feature map. Then these maps are fused to improve the performance of the model. On the other hand, an ensemble of multiple classifiers reduces the overfitting problem by combining multiple models prediction matrices. In this work, the prediction matrices of two Logsoftmax multiclass classifiers are combined. The feature maps for these two classifiers are extracted using pretrained DenseNet and ResNet. This study compares the late fusion of high‐level features approach and ensemble of multiple classifiers approach for object classification problems. Experimentation has been carried out on two benchmark datasets, such as CIFAR‐10 and CIFAR‐100, and it achieves 96.48% and 83.33% of test accuracy for ensemble of multiple classifiers and the late feature fusion approach. The proposed method has been compared with other deep architectures and datasets.