Image retrieval
Image Search Methods
- Image search is a specialized data search used to find images.
- Query terms such as keywords, image file/link, or clicking on an image can be used for searching.
- The system returns images similar to the query based on criteria like meta tags, color distribution, and region/shape attributes.
- Image meta search allows searching based on associated metadata such as keywords and text.
- Content-based image retrieval (CBIR) retrieves images based on their contents, such as textures, colors, and shapes, without relying on textual descriptions.
Data Scope
- Understanding the scope and nature of image data is crucial for designing an image search system.
- Search data can be classified into categories such as archives, domain-specific collections, enterprise collections, personal collections, and web images.
- Archives usually contain large volumes of structured or semi-structured homogeneous data.
- Domain-specific collections provide access to controlled users with specific objectives, like biomedical and satellite image databases.
- Enterprise collections are heterogeneous collections accessible within an organization's intranet, with images stored in various locations.
Evaluations
- Evaluation workshops aim to investigate and improve the performance of image retrieval systems.
- ImageCLEF is a track of the Cross Language Evaluation Forum that evaluates systems using both textual and pure-image retrieval methods.
- Content-based Access of Image and Video Libraries is a series of IEEE workshops from 1998 to 2001.
- These evaluations contribute to advancing the field of image retrieval and enhancing system capabilities.
Related Concepts
- Automatic image annotation is a related concept in image retrieval.
- Computer vision plays a significant role in image retrieval systems.
- Concept-based image indexing is another approach to organizing and retrieving images.
- Digital asset management involves the storage and organization of digital images.
- Digital image editing is often performed on retrieved images for various purposes.
References and Resources
- B E Prasad, A Gupta, H-M Toong, and S.E. Madnick developed the first microcomputer-based image database retrieval system at MIT in the 1990s.
- A 2008 survey article by Datta, Joshi, Li, and Wang documented the progress in image retrieval systems.
- Camargo, Caicedo, and Gonzalez proposed a kernel-based framework for image collection exploration in 2013.
- The references provide further reading and resources for understanding image retrieval.
- External links like Image-Net.org and VGG Image Search Engine offer additional tools and information related to image retrieval.