Image Retrieval and Linguistic Indexing

Summary

In a typical CBIR system, an image is represented by a collection of imagery features. The similarity between two images is captured by a similarity measure. Given a query image, target images are ranked according to the similarity with respect to the query. Different features and similarity measures have been discussed. In general, high feature similarity may not correspond to semantic similarity because of the semantic gap. We review two classes of techniques for narrowing the semantic gap: relevance feedback and image database preprocessing using statistical classification.

Linguistic indexing of images refers to the labeling of images using hundreds of possible linguistic terms. Image categorization puts an image into one of the predefined categories. Both tasks are important to CBIR, computer object recognition, and image understanding. This chapter provides a survey of the prior work in these areas.

Every child is an artist. The problem is how to remain an artist once we grow up. —Pablo Picasso (1881–1973)