Multi-Resolution Image Similarity Learning: A Method for Extracting Comprehensive Image Features

Pp: 213-224 (12)

* (Excluding Mailing and Handling)

Abstract

This research presents an image similarity learning method that focuses on extracting multi-resolution features from images. The proposed method involves a series of steps, including image collection, normalization processing, image pairing based on visual judgment and a Hash algorithm, and division of data into training and testing sets. Furthermore, a network model is constructed using a deep learning framework, and a specific objective function and optimizer are designated for similarity learning. The network model is then trained and tested using the prepared data sets. This method addresses several challenges encountered in conventional image similarity learning, such as limited feature information extraction, inadequate description of image features, limitations imposed by data volume during network training, and susceptibility to overfitting.

Keywords: Deep learning, Data set division, Image similarity learning, Multiresolution features, Network model, Overfitting.

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