Implementing a Content-Based Image Retrieval (CBIR) System in MATLAB

In this article, we will explore methods for creating a Content-Based Image Retrieval (CBIR) system using MATLAB. CBIR stands for Content-Based Image Retrieval, a technique that searches for and retrieves similar images by comparing image features rather than text labels. This technology is highly valuable in image processing and computer vision fields, with applications in medical image analysis, security surveillance, and entertainment industries. When developing a CBIR system in MATLAB, you need to understand fundamental concepts in image processing and computer vision, including image feature extraction, similarity measurement algorithms, and classification methods. Additionally, familiarity with MATLAB's Image Processing Toolbox and Machine Learning Toolbox is essential for effective implementation. Key functions like `imread()` for image loading, `extractLBPFeatures()` for texture analysis, and `pdist2()` for similarity calculations are commonly used in CBIR implementations. To enhance the accuracy and efficiency of CBIR systems, various techniques can be employed such as local feature extraction using SURF detectors (`detectSURFFeatures()`), color histogram analysis with `imhist()`, and neural network approaches using Deep Learning Toolbox functions. These methods help process image data more effectively, improving system performance and retrieval precision through algorithmic optimization and proper feature space organization. In summary, creating a CBIR system in MATLAB presents an engaging and challenging task that deepens understanding of image processing and computer vision fundamentals. This article aims to provide comprehensive insights into CBIR technology while offering valuable references for future research and practical implementations, including code structure planning and performance evaluation methodologies.