Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Preference Net: Image Recognition using Ranking Reduction to Classification

Version 1 : Received: 7 December 2021 / Approved: 8 December 2021 / Online: 8 December 2021 (14:43:39 CET)

How to cite: Elgharabawy, A.; Prasad, M.; Lin, C. Preference Net: Image Recognition using Ranking Reduction to Classification. Preprints 2021, 2021120140 (doi: 10.20944/preprints202112.0140.v1). Elgharabawy, A.; Prasad, M.; Lin, C. Preference Net: Image Recognition using Ranking Reduction to Classification. Preprints 2021, 2021120140 (doi: 10.20944/preprints202112.0140.v1).

Abstract

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Keywords

Image Recognition; Preference Net

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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