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Matching and Retrieval System Based on Vocabulary and Grammar of Color Patterns

Matching and Retrieval System Based on Vocabulary and Grammar of Color Patterns. By Pradeep C.Venkat Srinath Srinivasan. The Problem. To design an intelligent perception based system for pattern matching and retrieval of patterns from a database

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Matching and Retrieval System Based on Vocabulary and Grammar of Color Patterns

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  1. 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 basedsystem 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 subjectivephenomenon of human perception? • Can these factors be generalized over all kinds of images?

  4. Our Approach - Vocabulary and Grammar • Vocabulary: 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 LabColorSpace 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.38-54. • 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.38-54.

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