Multi-Resolution Image Similarity Learning: A Method for Extracting Comprehensive Image Features
- Authors: Sheradha Jauhari1, Sansar Singh Chauhan2, Gunajn Aggarwal3, Amit Gupta4, Navin Garg5
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View Affiliations Hide Affiliations1 Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India 2 Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, India 3 Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University. Greater Noida, India 4 Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India 5 Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
- Source: A Practitioner's Approach to Problem-Solving using AI , pp 213-224
- Publication Date: October 2024
- Language: English
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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.
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