4. Color Feature Extraction
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4.3 Image Clustering and Segmentation
- 4.3.1 K-Means Clustering
- 4.3.2 JSEG Segmentation
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4.4 Color Feature Extraction
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4.4.1 Color Histogram
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Description of color distribution.
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Bin creation and pixel counting.
- Ways to compute histograms
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4.4.1.1 Component Histogram
- Splitting into individual R, G, B channels.
- Concatenation of individual channel histograms.
- Color quantization to reduce histogram length.
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4.4.1.2 Indexed Color Histogram
- Quantizing the RGB color space into N colors (global palette).
- Histogram creation using indexed colors as bins.
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4.4.1.3 Dominant Color Histogram
- Creating the histogram from a native palatte
- Using adaptive/native palettes created from the image itself.
- Dominant color extraction (thresholding or K-means).
- Advantages and limitations of histograms.
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4.4.2 Color Structure Descriptor
- Addressing the lack of spatial information in histograms.
- Using a structuring element (window) for color counting.
- Color Structure (CS) histogram computation.
- Multiplying effect on isolated vs. grouped colors.
- Robustness and dependence on window size.
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4.4.3 Dominant Color Descriptor (DCD)
- Based on visually interpreting images using dominant colors.
- DCD extraction: histogram thresholding, (ci, pi) representation.
- Region-based DCDs for spatial information.
- Translation to color names.
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4.4.4 Color Coherence Vector
- Incorporating spatial information into histograms.
- Dividing histogram bins into coherent and non-coherent components.
- CCV computation procedure.
- Advantages and limitations.
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4.4.5 Color Correlogram
- Color version of gray level co-occurrence matrix.
- Characterizing the distribution of color pairs.
- 3D histogram representation (color pairs, spatial distance).
- Correlogram computation.
- Autocorrelograms.
- Matching and application
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4.4.6 Color Layout Descriptor
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Capturing the frequency of color changes (similar to texture).
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CLD computation:
- Image partitioning (8x8 grid).
- Color quantization (dominant color per block).
- DCT transform.
- Zigzag scanning and coefficient selection.
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Scalability and limitations.
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- 4.4.7 Scalable Color Descriptor
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Using wavelet transforms to create scalable histogram.
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Computing steps.
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Advantages.
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- 4.4.8 Color Moments
- Descriptive statistics for color images.
- Mean, variance, skewness, etc.
- Computation from individual channels or histograms.
- Concatenation.
- Advantages and limitations (concise but inaccurate).
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4. 5 Summary
- Importance of color space choice.
- Role of histogram in color feature extraction.
- Scalability and invariance properties of histograms.
- Matching histograms (simple vs. quadratic distance).
- Computation cost of different descriptors.
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4.6 Excercises
- Calculate histogram, log transform,
- Generate histograms, decompose images.