Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture. This "Blobworld " representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions which roughly correspond to objects, we allow querying at the level of objects rather than global image properties. We present results indicating that querying for distinctive objects using Blobworld produces significantly higher precision than does querying using color and texture histograms of the entire image.
|
4344
|
Maximum likelihood from incomplete data via the EM algorithm
– Dempster, Laird, et al.
- 1977
|
|
925
|
Color indexing
– Swain, Ballard
- 1991
|
|
841
|
Estimating the dimension of a model
– Schwarz
- 1978
|
|
699
|
Modeling by shortest data description
– Rissanen
- 1978
|
|
531
|
The design and use of steerable filters
– Freeman, Adelson
- 1991
|
|
383
|
Photobook: Contentbased manipulation of image databases
– Pentland, Picard, et al.
- 1996
|
|
369
|
Stochastic Complexity
– Rissanen
- 1989
|
|
297
|
Unsupervised Texture Segmentation Using Gabor Filters
– Jain, Farrokhnia
- 1991
|
|
271
|
Color Science: concepts and methods, quantitative data
– Wyszecki, Stiles
- 1982
|
|
223
|
Chabot: Retrieval from a relational database of images
– Ogle, Stonebraker
- 1995
|
|
222
|
NeTra: A toolbox for navigating large image databases
– Ma, Manjunath
- 1999
|
|
220
|
Preattentive texture discrimination with early vision mechanisms
– Malik, Perona
- 1990
|
|
209
|
et al., “Query by image and video content: the QBIC system
– Flickner
- 1995
|
|
202
|
Fast multiresolution image querying
– Jacobs, Finkelstein, et al.
- 1995
|
|
195
|
Blobworld: A system for region-based image indexing and retrieval
– Carson, Thomas, et al.
- 1999
|
|
188
|
Signal Processing for Computer Vision
– Granlund, Knutsson
- 1995
|
|
188
|
Image indexing using color correlograms
– Huang, Kumar, et al.
- 1997
|
|
182
|
Visual information retrieval
– Gupta, Jain
- 1997
|
|
181
|
Efficient color histogram indexing for quadratic form distance functions
– Hafner, Sawhney
- 1995
|
|
172
|
Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding
– Ayer, Sawhney
- 1995
|
|
140
|
Tools and techniques for color image retrieval
– Smith, Chang
- 1996
|
|
120
|
A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models
– Weiss, Adelson
- 1996
|
|
118
|
Adaptive segmentation of MRI data
– Wells, Kikinis, et al.
|
|
110
|
Color- and Texture-based Image Segmentation using EM and its application to Content-based Image Rtrieval
– Belongie, Carson, et al.
- 1998
|
|
93
|
Region-based image querying
– Carson, Belongie, et al.
- 1997
|
|
71
|
Query by image example: the CANDID approach
– Kelly, Cannon, et al.
- 1995
|
|
70
|
Markov random field models for unsupervised segmentation of textured color images
– Panjwani, Healey
- 1991
|
|
69
|
A framework for low level feature extraction
– Förstner
- 1994
|
|
65
|
Relevance feedback and other query modification techniques
– Harman
- 1992
|
|
59
|
Color- and texture-based image segmentation using the expectation-maximization algorithm and its application to contentbased image retrieval
– Belongie, Carson, et al.
- 1998
|
|
59
|
Multidimensional orientation estimation with application to texture analysis and optical flow
– Bign, Granlund, et al.
- 1991
|
|
58
|
Combining greyvalue invariants with local constraints for object recognition
– Schmid, Mohr
- 1996
|
|
53
|
Body plans
– Forsyth, Fleck
- 1997
|
|
44
|
Query analysis in a visual information retrieval context
– Enser
- 1993
|
|
44
|
Direct computation of shape cues using scale-adapted spatial derivative operators
– G˚arding, Lindeberg
- 1996
|
|
43
|
Configuration based scene classification and image indexing
– Lipson, Grimson, et al.
- 1997
|
|
39
|
Automatic and semi-automatic methods for image annotation and retrieval in QBIC
– Ashley, Barber, et al.
- 1995
|
|
38
|
Laws of organization in perceptual forms
– Wertheimer
- 1923
|
|
36
|
Single color extraction and image query
– Smith, Chang
- 1995
|
|
31
|
Detecting, localizing and grouping repeated scene elements from an image
– Leung, Malik
- 1996
|
|
29
|
Spectral covariance and fuzzy regions for image indexing
– Stricker, Dimai
- 1997
|
|
23
|
Neuro-Fuzzy and Soft Computing
– Jang, Sun, et al.
- 1997
|
|
21
|
The capacity and the sensitivity of color histogram indexing
– Stricker, Swain
- 1994
|
|
18
|
Searching for digital pictures
– Forsyth, Malik, et al.
- 1997
|
|
16
|
A framework for low level feature extraction
– Foerstner
- 1994
|
|
14
|
Multiscale annealing for real--time unsupervised texture segmentation
– Puzicha, Buhmann
- 1997
|
|
12
|
ªMaximum Likelihood from Incomplete Data via the EM
– Dempster, Laird, et al.
- 1977
|
|
11
|
et al., “Automatic and Semiautomatic Methods for Image Annotation and Retrieval
– Ashley
- 1995
|
|
6
|
ªPhotobook: ContentBased Manipulation for Image Databases,º Int'l
– Pentland, Picard, et al.
- 1996
|
|
5
|
ªColor Indexing,º Int'l
– Swain, Ballard
- 1991
|
|
