Implementation of Content-Based Image Retrieval Using Artificial Neural Networks
Sarath Yenigalla, K. Srinivasa Rao, Ngangbam Phalguni Singh
Abstract
CBIR (Content Based Image Retrieval) has become a critical domain in the previous decade, owing to the rising demand for image retrieval from multimedia databases. Typically, we take low-level (colour, texture and shape) or high-level (when machine learning techniques are used) features out of the photos. In our research, we examine the CBIR system utilising three machine learning methods, namely SVM (Support Vector Machine), KNN (K Nearest Neighbours), and CNN (Convolution Neural Networks), using Corel 1K, 5K, and 10K databases, by splitting the data into 80% train data and 20% test data. Moreover, 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.