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IMPLEMENTATION OF CONTENT-BASED IMAGE RETRIEVAL USING ARTIFICIAL NEURAL NETWORKS
* 1 , 2 , 2
1  Research Scholar KLU, Vijayawada, Andhra Pradesh, India
2  Multi-Core Architecture Computation (MAC) Lab, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, India
Academic Editor: VIJAYAKUMAR ANAND

Published: 13 March 2023 by MDPI in Holography Meets Advanced Manufacturing session Industry Application posters
Abstract:

CBIR (Content Based Image Retrieval) is an essential domain, especially in the last decade, due to the increased need for image retrieval from the multimedia database. In general, we extract low-level (color, texture, and shape) or high-level features (when we include machine learning techniques) from the images. In our work, we compare the CBIR system using three algorithms based on machine learning, i.e., SVM (Support Vector Machine), KNN (K Nearest Neighbors), and CNN (Convolution Neural Networks) algorithms using Corel 1K,5K,10K databases, by dividing the data into 80% train data and 20 % test data. Also, compare each algorithm’s accuracy and efficiency when a specific task of image retrieval is given to it. The final outcome of this project will provide us with a clear vision of how effective deep learning, KNN, and CNN algorithms are to finish the task of image retrieval.

Keywords: CBIR (Content Based Image Retrieval), SVM, KNN, CNN
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