1. By Pradeep C.Venkat Srinath Srinivasan
Matching and Retrieval System Based on Vocabulary and Grammar of Color Patterns By
Pradeep C.Venkat
Srinath Srinivasan

2. The Problem
To design an intelligent perception based system for pattern matching and retrieval of patterns from a database
System must retrieve the closest match(es) in terms of ‘similarity’ to a user-inputted query image
Matching must be done so as to emulate human perception to the extent possible

3. Mimicking Humans is a Tough Job!
How do humans judge ‘similarity’ of images?
What factors would best characterize the subjective phenomenon of human perception?
Can these factors be generalized over all kinds of images?

4. Our Approach - Vocabulary and Grammar
Four perceptual criteria (Mojsilovic, et al.) were identified for comparison of color patterns:
Overall color
Color purity
Regularity and Placement
Directionality
Grammar:
A set of rules governing the use of these criteria in judging similarity of patterns

5. Overview of the System Image Decomposition Estimation of Color
Distribution
Image
Database
Generation of
Pattern Map
Similarity Measurement
Feature Extraction
query
Extraction of
Texture
Primitives
Estimation of
Primitive
Distribution
Similarity
Judgment

6. Steps for Feature Extraction (Color Based)
The input image is transformed into the Lab Color Space for compact perceptually based color representation
Color distribution is determined using a vector quantization approach
Significant features are determined from the histogram
Color features are used in conjunction with an L2-norm distance measure to determine similarity

7. Steps for Feature Extraction (Texture Based)
Spatial smoothing to remove background noise
Construction of the achromatic pattern map (APM)
Construction of an edge map from the APM
Orientation processing to extract the distribution of pattern contours along different spatial directions
Computation of scale-spatial texture edge distribution

8. The Database
Our database consisted of over 300 images of color patterns, sceneries, buildings, plants, etc.

9. Results (Obvious Matches)
Query
Closest Matches

10. Results (contd.) - Obvious Matches
Query
Closest Matches

11. Results contd. (Non Obvious Matches?)
Query
Closest Matches

12. References
A. Mojsilovic, et. al, Matching and retrieval based on vocabulary and grammar of color patterns, IEEE Trans. Image Processing, vol. 9, no. 1 (Special issue on Digital Libraries), Jan. 2000, pp
A. Mojsilovic, et. al, The vocabulary and Grammar of Color Patterns, IEEE Trans. Image Processing, vol. 9, no. 3 (Special issue on Digital Libraries), Jan. 2000, pp