1 MPEG-7 DCD using Merged Palette Histogram Similarity Measure Lai-Man Po and Ka-Man Wong ISIMP 2004 Oct 20-22, Poly U, Hong Kong Department of Electronic.

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Presentation transcript:

1 MPEG-7 DCD using Merged Palette Histogram Similarity Measure Lai-Man Po and Ka-Man Wong ISIMP 2004 Oct 20-22, Poly U, Hong Kong Department of Electronic Engineering City University of Hong Kong

2 MPEG-7 Dominant Color Descriptor A compact and effective descriptor Generated by GLA color quantization Maximum of 8 colors in storage

3 Dominant Color Descriptor Similarity measure A modified Quadratic Histogram Distance Measure (QHDM) Since each DCD may have different set of colors, QHDM is used to account for identical colors and similar colors. Percentage p color Percentage q color

4 DCD-QHDM upper bound problem Limitations of QHDM - 1 Distance upper bound is varied by number of matching colors Completely different image cannot be identified by its upper bound F 2 1/2 I 2 F 3 1/3 I 3 F 1 1/2 I 1 F 1 I 1

5 DCD-QHDM upper bound problem Analysis of problem 1 The upper-bound of the distance measured varies by number of color in the descriptor Maximum of positive part is not a constant Maximum of negative part is zero So, the maximum of QHDM result is not fixed This property makes DCD unable to identify completely different images by the values measured Positive partNegative part

6 DCD-QHDM Similarity coefficient problem Limitations of QHDM - 2 The similarity coefficient does not well model color similarity It does not balance between color distance and area of matching I 4 F 4 1 F 2 1/2 I 2 F 1 I 1 F 1 I 1

7 DCD-QHDM Similarity coefficient problem The similarity coefficient use the color distance to fine tune the similarity Difficult to define a quantitative similarity between colors, Sensitivity of human eye depends on many conditions (e.g. light source of the room, spatial layout of the image, etc.) a = 16.67% a = 44% T d a = 0% 1.2  d

8 Proposed Merged Palette Histogram Similarity Measure MPHSM Process - 1 Find the closest pair of colors using Euclidian distance in CIELuv color space MPHSM process - 2 If the distance smaller than a threshold T d, merge them to form a new common palette color Common Palette

9 Proposed Merged Palette Histogram Similarity Measure MPHSM process - 3 A new common palette is then generated Form new descriptors based on the common palette Dominant Color Descriptor Common Palette Merged Palette Histogram

10 Proposed Merged Palette Histogram Similarity Measure MPHSM process - 4 Histogram intersection is used to measure the similarity Count the non-overlapping area as the distance

11 Flow of MPHSM Initial DCDs Step 1: Find a pair of colors with minimum distance d d<T d ? Step2: Merge colors having minimum distance Common Palette N Y Step 3: Update each DCD based on the common palette Step 4: Histogram Intersection

12 Experiment Result of MPH-RF Experiment Methodology ANMRR Image Database 5466 Images from MPEG-7 common color dataset (CCD) 50 Pre-defined query and ground truth sets

13 Latest experimental results MPHSM without spatial coherence improves DCD by about 0.04 of ANMRR in average Very close to QHDM with spatial coherence Significant improve in medium queries It gives significant improvement on visual results *ANMRR (smaller means better) QHDMQHDM-SCMPHSMMPHSM-SC easy queries NMRR<0.2 (17) hard queries NMRR > 0.4 (12) medium queries (21) average queries (50)

14 Experimental results Visual results - Query #32 from MPEG-7 CCD Demo available in Query image QHDM results, ANMRR=0.4MPHSM result, ANMRR=0.0111

15 Experimental results Visual results - Query #25 from MPEG-7 CCD Demo available in Query image QHDM results, ANMRR=0.3935MPHSM result, ANMRR=0.0481

16 Conclusion A new merged palette histogram similarity measure for dominant color descriptor of MPEG-7 is proposed The merged palette formed a common color space and used to redefine the new query histograms for histogram intersection similarity measure. Can match identical colors as well as similar colors Use area of matching for similarity measure

17 Conclusion Experimental results show that the proposed MPHSM improve DCD-QHDM using ANMRR rating by about 0.04 and very close to the result of DCD-QHDM with spatial coherence Our experiment result also found that the result of proposed method can be further improved by spatial coherence The proposed method also provide better perceptually relevant image retrieval.