1. 10/24/2015 Content-Based Image Retrieval: Feature Extraction Algorithms EE-381K-14: Multi-Dimensional Digital Signal Processing BY:Michele Saad EMAIL:michele.saad@mail.utexas.edumichele.saad@mail.utexas.edu PROF:Brian L. Evans

2. 10/24/2015 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

3. 10/24/2015 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

4. 1 10/24/2015

5. 10/24/2015 Color Features Color FeatureProsCons Color Space 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 HSV J. 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

6. 10/24/2015 Color Features Cont’d Color FeatureProsCons Color Space CorrelogramSpatial 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 HSV N. 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.

7. Color Image Database: The Corel Database http://wang.ist.psu.edu/IMAGE 10/24/2015 10 classes of 100 images each

8. 10/24/2015 Color Feature Extraction: Retrieval Results CCHFCHCorrelogramColor/Shape AverageRetrievalScore 80.12%82.05%69.48%70.03% NB: Euclidean distance measure used

9. 2 10/24/2015

10. Texture Features Texture Feature ProsCons Frequency 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

11. 10/24/2015 Texture Features Cont’d Texture Feature ProsCons Frequency 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

12. 10/24/2015 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

13. 10/24/2015 Texture Feature Extraction: Retrieval Results Steerable Pyramid Contourlet Transform Gabor Complex Directional Filter Bank AverageRetrievalScore 63.02%63.67%81.48%76% NB: L 1 Norm used in the distance measure

14. 10/24/2015 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