04/20/23

Content-Based Image Retrieval:

Feature Extraction Algorithms

EE-381K-14: Multi-Dimensional Digital Signal Processing

BY: Michele Saad EMAIL: michele.saad@mail.utexas.eduPROF:Brian L. Evans

04/20/23

Motivation• Increased use of image and video

– Education– Entertainment– Commercial purpose

• Need for efficient and effective browsing into image databases

• Need for reduction of semantic gap between low-level features and high-level user semantics

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Objectives and Contributions

• Objective: – Implementation and comparison of texture

and color feature extraction algorithms

• Contribution:– An up-to-date comparison of state-of-the-

art texture and color feature extraction methods

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Color Features

Color Feature

Pros ConsColor Spac

e

Conventional Color

histogram

•Fast computation•Simple

•High dimensionality•No color similarity•No spatial info

HSV

Fuzzy Color Histogram

•Fast Computation•Color similarity•Robust to quantization noise •Robust to contrast

•High dimensionality•More computation•Appropriate choice of membership weights needed

HSVJ. Huang, S. R. Kumar, M. Mitra, W. J. Zhu and R. Zabih, “Time Indexing Using Color Correlograms”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 762 – 768, June 1997

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Color Features Cont’d

Color Feature

Pros ConsColor Spac

e

Correlogram •Spatial Info

•Very slow•High dimensionality•No color similarity

HSV

Color/Shape Method

•Spatial info•Area•Shape

•More computation•Sensitive to clutter•Choice of appropriate color quantization thresholds needed

HSVN. R. Howe, D. P. Huttenlocher, “Integrating Color, Texture and Geometry for Image Retrieval”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 239-246, June 2000.

Color Image Database:The Corel Database

http://wang.ist.psu.edu/IMAGE04/20/23

• 10 classes of 100 images each

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Color Feature Extraction:Retrieval Results

CCH FCHCorrelogram

Color/Shape

Avera

ge R

etrie

val

Score

80.12%

82.05%

69.48% 70.03%NB: Euclidean distance measure used

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Texture Features

Texture Feature

Pros ConsFrequency Domain Partition

Steerable Pyramid

•Supports any number of orientation

•Sub-bands undecimated

Contourlet

Transform

•Lower sub-bands decimated

•Number of orientations is a power of 2

S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64, Oct. 2007

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Texture Features Cont’d

Texture Feature

Pros ConsFrequency

Domain Partition

Gabor Wavelet

•Highest retrieval results

•Over-complete representation•Computationally intensive

Complex Directional Filter Bank

•Competitive retrieval results

•More computation

S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64, Oct. 2007

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Texture Database: The Brodatz Database

• 13 different textures:– Bark, brick, bubbles, grass, leather, pigskin,

raffia, sand, straw, water, weave, wood and wool

– Rotated at different angles

• Examples:

http://www.ux.uis.no/~tranden/brodatz.html

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Texture Feature Extraction:Retrieval Results

Steerable Pyramid

Contourlet Transform

Gabor

Complex Directional Filter Bank

Avera

ge R

etrie

val

Score

63.02%

63.67%81.48

%76%

NB: L1 Norm used in the distance measure

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Conclusion and Future Work

• Highest retrieval results obtained by:– Fuzzy color histogram– Gabor wavelet transform

• Keeping in mind some trade offs• Appropriate distance measures

need to be considered further– May improve results further