Increasing the Accuracy of Convolutional Neural Networks Based on 4-Stream Model with Image Processing Filters and Linear Mapping of Dissimilarity Space

Subject Areas : Image and video processing

Zahra  Heydaran Daroogheh Amnyieh ¹ (Department of Electrical Engineering- Saveh Branch, Islamic Azad University, Saveh, Iran)
Seyed Mohammad Jalal  Rastegar Fatemi ² (Department of Electrical Engineering- Saveh Branch, Islamic Azad University, Saveh, Iran)
Maryam  Rastgarpour ³ (Department of Computer Engineering- Saveh Branch, Islamic Azad University, Saveh, Iran)
Golnaz  Aghaee ghazvini ⁴ (Department of Computer Engineering- Dolatabad Branch, Islamic Azad University, Dolatabad, Iran)

Received: 2022-08-26 Accepted : 2022-12-14 Published : 2022-12-14

Keywords: prototype, deep learning, Representational Dissimilarity Matrix, convolutional neural network, Dissimilarity Vector Space,

Abstract :

With the expansion and success of convolutional networks, the topic of deep learning has attracted increasing attention in recent years; Since convolutional networks include many layers, optimal learning of network layers is of great importance. In this paper, a new model, called the 4-stream model, is presented with the aim of helping to linearize the data space using representational dissimilarity transformation, and the effects of this transformation on standard classifications for artificial data and Cifar10 images are investigated. Then, two models based on data preprocessing with dissimilarity transform representation and Sobel and Edge Detector filters are analyzed. The 4-stream model increased the accuracy by 3.2% due to the increase in the number of model parameters, and hence the capacity of the network. Besides, adding the dissimilarity representation wherever the classifier cannot perform a high-resolution classification by merely using the main features, can help to increase the discriminability of classes by adding linear features.

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