Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods.

The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user. At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.

See also Content-based image retrieval Object categorization from image search Object detection Outline of object recognition

References

Further reading

Word co-occurrence model

Annotation as machine translation

Statistical models

Automatic linguistic indexing of pictures

Hierarchical Aspect Cluster Model

Latent Dirichlet Allocation model

Supervised multiclass labeling

Texture similarity

Support Vector Machines

Ensemble of Decision Trees and Random Subwindows

Maximum Entropy

Relevance models

Relevance models using continuous probability density functions

Coherent Language Model

Inference networks

Multiple Bernoulli distribution

Multiple design alternatives

Image captioning

Natural scene annotation

Relevant low-level global filters

Global image features and nonparametric density estimation

Video semantics

Image Annotation Refinement

Automatic Image Annotation by Ensemble of Visual Descriptors

A New Baseline for Image Annotation

Simultaneous Image Classification and Annotation

TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation

Image Annotation Using Metric Learning in Semantic Neighbourhoods

Automatic Image Annotation Using Deep Learning Representations

Holistic Image Annotation using Salient Regions and Background Image Information

Medical Image Annotation using bayesian networks and active learning