Performance Evaluation of Content-Based Image Retrieval Using Block Truncation Coding and CNN
Ajinkya P. Nilawar · Bioscience Biotechnology Research Communications · 2020
Retrieving similar images from a large image database is a critical task.The solution to this problem is the use of a Content-Based Image Retrieval System.The images are described through their content, there is three predominant content existing in an image like color, shape, and texture.In this paper, we are evaluating the performance of the CBIR system using two methods.The first method consists of Block Truncation coding (BTC) with Grey level co-occurrence matrix (GLCM).The second method consists of Use of CNN.The feature extraction technique is achieved based on an input query image from the database and features are saved in a feature dataset.A proposed strategy retrieves similar images from a database that fulfills the user's desire.The similarity measurement can be done using the Euclidean distance and hashing technique.The overall performance of the retrieval system has been analyzed through the parameters Precision and Mean Average Precision.The experimental result shows encouraging results using CNN which leads to improving accuracy.