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Modeling Spatial-Chromatic Distribution for CBIR

Modeling Spatial-Chromatic Distribution for CBIR. NTUT CSIE D.W. Lin 2004.3.19. Outlines. Review Incorporating shape into color information Geometry enhanced color histogram Modeling spatial-chromatic distribution Nakagami-m distribution Refining the modeling efficiency

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Modeling Spatial-Chromatic Distribution for CBIR

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  1. Modeling Spatial-Chromatic Distribution for CBIR NTUT CSIE D.W. Lin 2004.3.19

  2. Outlines • Review • Incorporating shape into color information • Geometry enhanced color histogram • Modeling spatial-chromatic distribution • Nakagami-m distribution • Refining the modeling efficiency • Experiment results at present • Feature works

  3. The integration of color and shape • Histogram refinement • Specific-color pixel distribution (single, pair, triple …) • Edge histogram … • Color histogram • Global color histogram • Global color histogram + spatial info. • Partition + local color histogram or color momnets • Dominant color • Extracting the representative colors of image via VQ or clustering (e.g. k-means algorithm) • Spatial info. can be attained Spatial or frequency

  4. Color Correlograms • For a nn with m colors image I, • The histogram is: • The correlogram is: • The autocorrelogram is:

  5. Geometry-enhanced color histogram • For image I1 and I2, the similarity between autocorrelograms with color j and distance k is: • GECH uses first distance moment to decorrelate the spatial information from autocorrelogram

  6. Autocorrelogram and GECH GECH, O(m) Color moment distance Modeled histogram , O(m) modeling 5 3 1 i color Autorrelogram, O(md)

  7. Modeling spatial-chromatic distribution • Complexity of feature vector • High dimension for bearing more info. • Nakagami-m distribution • Adequate pixels for a perceptible color region • Clustering phenomenon for meaningful color region (thus the beginning may not be zero) • Variety of distribution curves capture the spatial information well

  8. Nakagami-m distribution Ω=E[R2], second moment , fading figure

  9. Modeling spatial-chromatic distribution – cont. • Parameters estimation for Nakagami-m • based on the maximum-likelihood • Using second order approximation

  10. Testset Berkeley14838 Berkeley150 Stanford th= 3, L2 87.51%(61.91%) 87.74%(60.91%) Pixel_th = 1%, L2 87.68%(62.11%) 88.55%(61.85%) Pixel_th = 2%, L2 88.22%(62.81%) 88.98%(62.52%) Th=3, L1 88.72%(58.20%) 90.95%(57.55%) 90.18%(56.27%) Th=1%, L1 88.93%(58.37%) 91.14%(57.75%) 91.06%(57.17%) Th=2%, L1 89.71%(59.06%) 91.74%(58.43%) 91.51%(57.79%) Modeling efficiency Metric: intersection, compared with uniform dist.

  11. Testset Nor Berkeley Stanford Pixel_threshold = 3 72.47% (0.39%) 86.51% (0.73%) 74.64% (0.27%) Pixel_th = 1% 79.79% (2.8%) 87.32% (1.13%) 82.85% (2.8%) Pixel_th = 2% 84.51% (7.2%) 89.26% (2.91%) 86.44% (6.1%) Modeling efficiency – cont. Threshold: percentage of total DC image pixels Entriey: rule out colors (pixels) in percentage

  12. Refining the modeling • At the first glance: • Remove the insignificant pixel • Find out the dominant cluster • Segmentation via MED (maximum entropy discrimination)

  13. MED • Max. entropy discrimination • discretization, classification, method(MEM) • Power spectrum estimation • For segmentation • b: SPMF • c: PMF (for max. entropy) • d: likelihood ratio

  14. MED – cont. • 20 observed values: 0.1, 0.9, 1.5, 2.0, 2.8, 3.2, 3.3, 3.5, 3.7, 3.8, 4.0, 4.5, 4.9, 5.5, 6.0, 7.3, 8.5, 8.8, 9.1, 9.5 Interval width = 9.5/4 = 2.375 p(I1) = (4/20)/2.375 = 0.084 Elements of Interval = 20/4 = 5 p(I1) = 0.25/(2.8-0.1) = 0.084 Equal-width-interval MED For uniform distribution

  15. Eta = 0.8183 Eta = 0.9563 Algorithm for refining modeling

  16. Remarks for the algorithm • Considerations: • Choice of parameters: number of intervals, constraints while merging the neighbor intervals • Sparse data, or scene with texture may be in vain • Concave region

  17. Conclusions and feature works • Other features that may satisfy Nagakami-m modeling (avoid biased by correlogram) • Similarity measure: Battachaya distance

  18. References • J. Cheng, N.C. Beaulieu, “Maximum-likelihood based estimation of the Nakagami m parameter,” IEEE Communications Letters, Vol. 5, No. 3, pp. 101-103, 2001 • S.-H. Yang and D.-W. Lin, “A geometry-enhanced color histogram,” IEEE Int’l Conf. Information: Research and Education, New Jersey, USA, Aug. 2003.

  19. References - MED • J.B. Jordan and L.C. Ludeman, “Image segmentation using maximum entropy technique,” Intl’ Conf. Acoustics, Speech and Signal Processing (ICASSP’84), Vol. 9, pp. 674-677, 1984 • Hsu , T.S. Chua, and H.K. Pung, “An integrated color-spatial approach to content-based image retrieval,” Proc. Of ACM Multimedia Conf., pp. 305-313, Nov. 1995 • T. Jaakkola, M. Meila, and T. Jebara, “Maximum entropy discrimination,” In Advance in Neural Information Processing Systems 12 MIT Press, 1999

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