Content-Based Image Retrieval Systems
Summary
Content-based image retrieval (CBIR) systems enable the search and organisation of large-scale visual databases by analysing intrinsic image features rather than relying on text annotations. At their core, these systems extract numerical representations—feature vectors—that characterise colour, texture, shape or learned patterns. These representations are indexed to support rapid similarity queries. Traditional CBIR architectures focus on low-level descriptors, yet the persistent semantic gap between these descriptors and human perception has driven the adoption of machine learning and deep learning approaches. Modern pipelines typically employ convolutional neural networks to derive high-level embeddings, which better align with semantic content. Retrieval employs distance metrics or graph-based ranking to filter and rank results. These methods find applications in medical diagnostics, digital cultural heritage, remote sensing, e-commerce and automated quality control. Recent advances emphasise computational efficiency, hierarchical multi-feature fusion and user-driven relevance feedback, reflecting a shift towards systems that are both accurate and responsive to diverse real-world demands.
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Content-Based Image Retrieval Systems publication trend
The graph below shows the total number of articles in content-based image retrieval systems across all publications each year (not limited to Nature Index journals).
Technical terms
Semantic gap: The discrepancy between machine-computed low-level image features and high-level human interpretation of visual content.
Feature vector: A numerical array representing quantifiable aspects of an image, used for indexing and similarity comparison.
Convolutional neural network (CNN): A layered deep learning architecture designed to learn spatial hierarchies of image features through convolutional filters and pooling operations.
Approximate Nearest Neighbour Search: An efficient indexing method that retrieves data points close to a query vector in high-dimensional feature spaces with sub-linear time complexity.
Hierarchical framework: A multi-stage retrieval structure that applies different feature extraction and filtering steps in sequence to improve both speed and accuracy.
References
- A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process. Journal of King Saud University - Computer and Information Sciences (2023).
- An efficient content based image retrieval framework using separable CNNs. Cluster Computing (2024).
- A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion. Applied Sciences (2023).
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