Overview

Today, vast collections of digital images are available across the world. To make effective use of these databases, efficient and reliable image retrieval techniques are essential. Traditionally, images were retrieved using text-based annotations, where each image was manually labeled and later searched through keywords. However, with the rapid growth in both the number and variety of images, this approach has become inefficient and often ambiguous. As a result, Content-Based Image Retrieval (CBIR) has gained significant attention. Since the early 1990s, CBIR has emerged as a vital area of research within the multimedia community, focusing on retrieving images directly based on their visual features rather than text descriptions. The rise of digital storage has led to massive repositories of unlabeled image data-stored both on the web and within networked systems-making automated image retrieval an increasingly important challenge. The widespread availability of smartphones and digital cameras has further accelerated the production of images, increasing the need for intelligent retrieval methods. Search engines such as Google, Bing, and Flickr now invest heavily in improving image search capabilities. Yet, one of the main obstacles remains the lack of consistent or accurate textual information for these images. Human labeling is often subjective, and different annotators may describe or interpret the same image in varying ways. This inconsistency highlights the necessity for robust, content-based approaches that rely on visual analysis rather than manual description.

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