An Appraisal of Content-Based Image Retrieval (CBIR) Methods

• Published in: Asian Journal of Research in Computer Science pp. 1 - 15
• Main Authors: Olaleke, J. O., Adetunmbi, A. O., Ojokoh, B. A., Olaronke, Iroju
• Format: Journal Article
• Language: English
• Published: 19.04.2019
• ISSN: 2581-8260; 2581-8260
• DOI: 10.9734/ajrcos/2019/v3i230089

Background: Content Based Image Retrieval (CBIR) is an aspect of computer vision and image processing that finds images that are similar to a given query image in a large scale database using the visual contents of images such as colour, texture, shape, and spatial arrangement of regions of interest (ROIs) rather than manually annotated textual keywords. A CBIR system represents an image as a feature vector and measures the similarity between the image and other images in the database for the purpose of retrieving similar images with minimal human intervention. The CBIR system has been deployed in several fields such as fingerprint identification, biodiversity information systems, digital libraries, Architectural and Engineering design, crime prevention, historical research and medicine. There are several steps involved in the development of CBIR systems. Typical examples of these steps include feature extraction and selection, indexing and similarity measurement. Problem: However, each of these steps has its own method. Nevertheless, there is no universally acceptable method for retrieving similar images in CBIR. Aim: Hence, this study examines the diverse methods used in CBIR systems. This is with the aim of revealing the strengths and weakness of each of these methods. Methodology: Literatures that are related to the subject matter were sought in three scientific electronic databases namely CiteseerX, Science Direct and Google scholar. The Google search engine was used to search for documents and WebPages that are appropriate to the study. Results: The result of the study revealed that three main features are usually extracted during CBIR. These features include colour, shape and text. The study also revealed that diverse methods that can be used for extracting each of the features in CBIR. For instance, colour space, colour histogram, colour moments, geometric moment as well as colour correlogram can be used for extracting colour features. The commonly used methods for texture feature extraction include statistical, model-based, and transform-based methods while the edge method, Fourier transform and Zernike methods can be used for extracting shape features. Contributions: The paper highlights the benefits and challenges of diverse methods used in CBIR. This is with the aim of revealing the methods that are more efficient for CBIR. Conclusion: Each of the CBIR methods has their own advantages and disadvantages. However, there is a need for a further work that will validate the reliability and efficiency of each of the method.