Image Search by Content
What is Content-Based Image Retrieval (CBIR)?
Our content-based image retrieval (CBIR) technology allows images to be searched based on their visual content rather than relying on traditional metadata such as file names or tags. CBIR enables images to be classified based on their appearance by analyzing pixels. With this approach, users can find images that are visually or semantically similar to a given example, regardless of how the image is labeled.
Computer Vision
At the core of CBIR are computer vision and deep learning algorithms that extract visual features from an image and convert them into vectors.
These vectors capture both low-level details (such as color and texture) and high-level semantics (like objects, scenes, or actions).
Specialized databases
All these image representations are stored in a specialized database optimized for fast similarity search.
When a user submits an image or a text-based query, the system compares it to the stored vectors and instantly identifies the closest matches.
Image Search by Content Applications
Whether you want to
enable image-to-image search, text-to-image search, or visual similarity
detection, our CBIR solutions are designed to deliver precise and
scalable performance across large image collections.
Digital Collection Discovery
Image search by similarity allows for a more intuitive discovery of visual heritage. This approach enables institutions to facilitate access to their collections, particularly for non-specialist audiences or documents with limited documentation, thereby promoting broader engagement.
Visual Analysis and Research
Duplicates Detection
Archives and libraries frequently face the challenge of organizing large image datasets where duplicates or near-identical reproductions exist. CBIR can automatically detect duplicate or near-duplicate images, flagging different versions and derivatives of the same work. It can also help track the evolution of digital restorations or identify missing or damaged elements in visual archives.
Image search by content & IIIF
By integrating CBIR with IIIF, institutions can leverage the interoperability and accessibility of IIIF image repositories while enabling content-based search and comparison. This combination supports seamless cross-collection research and promotes open access to cultural heritage resources.
