<title>QBIC project: querying images by content, using color, texture, and shape</title>

TLDR

QBIC explores content‑based image querying for applications in medicine, photo‑journalism, art, fashion, cataloging, retail, and industry. The project aims to develop and present algorithms for querying large image databases by color, texture, shape, and sketch, and to demonstrate their effectiveness. Algorithms compute image attributes, use similarity‑based retrieval, and provide interfaces for example or sketch queries, with high‑dimensional indexing and semi‑automatic database population. A prototype system in X/Motif and C on an RS/6000 supports diverse queries over a 1000‑image/1000‑object database and yields example results.

Abstract

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.