1996
@inproceedings{PaZ1996,
vgclass = {refpap},
vgproject = {cbir},
author = {Greg Pass and Ramin Zabih},
title = {Histogram Refinement for Content-Based Image Retrieval},
booktitle = {IEEE Workshop on Applications of Computer Vision},
address = {Sarasota, Florida, USA},
pages = {96--102},
month = {December},
year = {1996},
abstract = {Color histograms are widely used for content-based image
retrieval. Their advantages are efficiency, and insensitivity to small
changes in camera viewpoint. However, a histogram is a coarse
characterization of an image, and so images with very different
appearances can have similar histograms. We describe a technique for
comparing images called histogram refinement, which imposes additional
constraints on histogram based matching. Histogram refinement splits
the pixels in a given bucket into several classes, based upon some
local property. Within a given bucket, only pixels in the same class
are compared. We describe a split histogram called a color coherence
vector (CCV), which partitions each histogram bucket based on spatial
coherence. CCV's can be computed at over 5 images per second on a
standard workstation. A database with 15,000 images can be queried
using CCV's in under 2 seconds. We demonstrate that histogram
refinement can be used to distinguish images whose color histograms are
indistinguishable.},
}