1997
@mastersthesis{Pec1997,
vgclass = {thesis},
vgproject = {cbir},
author = {Zoran Pe\u{c}enovi\'{c}},
title = {Image retrieval using Latent Semantic indexing},
type = {Final year graduate thesis},
school = {AudioVisual Communications Lab, Ecole Polytechnique
F\'{e}d\'{e}rale de Lausanne},
address = {Switzerland},
month = {June},
year = {1997},
abstract = {Image Retrieval is a domain of increasing and crucial
importance in the new information based society. Large and distributed
collections of scientific, artistic, technical and commercial images
are becoming a common ground, thus requiring sophisticated and precise
methods for users to perform similarity and semantic based queries.
Much work has already been done in this direction, but the majority is
either too specific or too general. Encouraging results have already
been achieved with semantic-based methods on textual information and
have given us a starting point for the investigation of the feasibility
of their application to image data.
Latent semantic indexing has been successfully used in very large
document collections, and its theoretical background allows for
interesting possibilities. Latent semantic indexing extracts the
underlying semantic structure, alleviates the variability, and reduces
noise in ``term'' usage. The difficult question is of course: what kind
of ``terms'' are images composed of? The work presented herein is the
result of our attempt to partially and subjectively answer this
question.
After making a choice of ``terms'' we have implemented a prototype
Latent semantic indexing image retrieval system. The collection of
images was fairly large (approximately 4'500 images) and contained
photographs and paintings of various very different subjects. Much
importance was further given to query construction and relevance
feedback in the system. The results were quite encouraging, and have
lead us to a set of bright new outlooks on future improvements and
extensions of the prototype.},
}