Abstract:
Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his-∗ To whom correspondence should be addressed 1 tograms for image retrieval.
Citations
|
925
|
Color indexing
– Swain, Ballard
- 1991
|
|
383
|
Photobook: Contentbased manipulation of image databases
– Pentland, Picard, et al.
- 1996
|
|
223
|
Chabot: Retrieval from a relational database of images
– Ogle, Stonebraker
- 1995
|
|
223
|
Automatic partitioning of full-motion video
– Zhang, Kankanhalli, et al.
- 1993
|
|
209
|
et al., “Query by image and video content: the QBIC system
– Flickner
- 1995
|
|
183
|
Lightness and Retinex Theory
– Land, McCann
- 1971
|
|
181
|
Efficient color histogram indexing for quadratic form distance functions
– Hafner, Sawhney
- 1995
|
|
176
|
Color constant color indexing
– Funt, Finlayson
- 1995
|
|
169
|
Automatic Video Indexing and Full-Video Search for Object Appearances
– Nagasaka, Tanaka
- 1992
|
|
140
|
Tools and techniques for color image retrieval
– Smith, Chang
- 1996
|
|
138
|
A Feature-Based Algorithm for Detecting and Classifying Production Effects
– Zabih, Miller, et al.
|
|
79
|
Color indexing with weak spatial constraints
– Stricker, Dimai
- 1996
|
|
76
|
Image processing on Compressed Data for Large Video Databases
– Arman, Hsu, et al.
- 1993
|
|
50
|
Automatic content-based retrieval of broadcast news
– Brown, Foote, et al.
- 1995
|
|
49
|
Production model based digital video segmentation
– Hampapur, R, et al.
- 1995
|
|
34
|
In Integrated Color-Spatial Approach to Content-Based Image Retrieval
– Hsu, Chua, et al.
- 1995
|
|
33
|
The capacity of color histogram indexing
– Stricker, Swain
- 1994
|
|
32
|
Natural Object Recognition
– Strat
- 1992
|
|
29
|
Pattern rejection
– Baker, Nayar
- 1996
|
|
18
|
Content-based image retrieval using color tuple histograms
– Rickman, Stonham
- 1996
|
|
8
|
Projection-detecting filter for video cut detection
– Otsuji, Tonomura
- 1994
|
