Identification and Improvement of Image Similarity using Autoencoder

Abstract

Identifying the similarity between fine-grained images requires sophisticated techniques. This study presents a deep learning approach to the image similarity problem as an unsupervised learning task. The proposed autoencoder, built on a Deep Neural Network (DNN), autonomously learns image representations by computing cosine similarity distances between extracted features. This paper presents several applications, including training the autoencoder, transforming images, and evaluating the DNN model. In each instance, the generated images exhibit sharpness and closely resemble natural photographs, demonstrating the effectiveness and versatility of the proposed deep learning framework in computer vision tasks. The results suggest that the proposed approach is well-suited for tasks that require accurate image similarity assessments and image generation, highlighting its potential for various applications in image retrieval, data augmentation, and pattern recognition. This study contributes to the advancement of the computer vision field by providing a robust and efficient method for learning image representations and evaluating image similarity in an unsupervised manner.