Image retrieval using image context vectors: first results

Steve Gallant; Michael Johnston

doi:10.1117/12.205319

23 March 1995 Image retrieval using image context vectors: first results

Steve Gallant, Michael Johnston

Proceedings Volume 2420, Storage and Retrieval for Image and Video Databases III; (1995) https://doi.org/10.1117/12.205319
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

The key question for any image retrieval approach is how to represent the images. We are exploring a new image context vector representation that avoids the need for full image understanding. This representation: (1) is invariant with respect to translation and scaling of (whole) images, (2) is robust with respect to translation, scaling, small rotations, and partial occlusions of objects within images, (3) avoids explicit segmentation into objects, and (4) allows computation of image-query similarity using only about 300 multiplications and additions. A context vector is a high (approximately 300) dimensional vector that can represent images, subimages, or image queries. Image context vectors are an extension of previous work in document retrieval where context vectors were used to represent documents, terms, and queries. The image is first represented as a collection of pairs of features. Each feature pair is then transformed into a 300-dimensional context vector that encodes the feature pair and its orientation. All the vectors for pairs are added together to form the context vector for the entire image. Retrieval order is determined by taking dot products of image context vectors with a query context vector, a fast operation. Results from a first prototype look promising. 119

Citation Download Citation

Steve Gallant and Michael Johnston "Image retrieval using image context vectors: first results", Proc. SPIE 2420, Storage and Retrieval for Image and Video Databases III, (23 March 1995); https://doi.org/10.1117/12.205319

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available