Image Feature Extraction: Shape, Color, and Texture Descriptors

Posted on Jan 19, 2026 in Electrical Engineering

Shape Descriptors

Contour-Based Shape Descriptors

DescriptorDescription
Shape SignatureRepresents the shape of an object as a one-dimensional function extracted from contour points. Examples include distance to centroid, tangent angle, curvature, or arc length. It provides a compact representation for shape analysis and matching.
Convex HullDefines the smallest convex polygon enclosing the shape. It simplifies complex shapes by excluding concavities, providing a more generalized outline. Useful for detecting shape boundaries or object encapsulation.
Wavelet TransformDecomposes the contour into hierarchical frequency components using wavelets. Higher-level components convey global shape features, while lower-level ones capture finer details. This descriptor supports multi-resolution analysis.
Minimum Boundary CircleUses the smallest enclosing circle of the shape to extract features like the center, radius, and crossing points of the circle. It is a simplified yet robust representation for compact objects.
Chain CodeEncodes the contour by recording directional movements between consecutive points in the shape’s boundary. Useful for compact shape representation and matching based on contour structure.
Polygons DecompositionDivides the contour into smaller convex polygons using vertices and line segments. Each segment is encoded with features such as vertex coordinates, angles, and distances for detailed structural analysis.
Shape ContextCaptures the spatial distribution of points along a shape’s contour in a log-polar histogram. The histogram reflects both global and local properties, facilitating robust shape matching and retrieval.

Region-Based Shape Descriptors

DescriptorDescription
Shape MatrixConverts a shape into a matrix representation by applying polar quantization. This matrix preserves key geometric properties while simplifying shape comparison and analysis.
Grid-Based DescriptorSuperimposes a grid over the shape and encodes binary values based on whether grid cells overlap with the shape. Shape similarity is determined by analyzing differences between binary grids using XOR operations.
Angular Radial Partitioning (ARP)Divides the normalized edge image into angular and radial segments, capturing the structural features of the shape for similarity measurement and efficient matching.
SkeletonRepresents the medial axis of a shape as a simplified graph structure. Calculated using the Medial Axis Transform (MAT), it captures the object’s topology and connectivity for structural analysis.

Motion Descriptors

DescriptorDescription
Optical FlowEstimates motion between consecutive frames by analyzing pixel intensity changes. Provides a dense motion field that reflects object or observer movement, enabling applications like video stabilization and tracking.
Block MatchingDivides images into blocks and tracks the movement of these blocks between frames by searching for the best match in a predefined region. Efficient for regular motion estimation, such as in video compression algorithms.
Phase CorrelationUses phase shifts in the frequency domain to estimate translations, rotations, and scale changes between frames. This method is computationally efficient and robust to noise.
Angular Circular MotionAnalyzes motion by dividing an object into angular and circular regions, measuring pixel variations in these segments. Useful for understanding spatio-temporal object deformations and movements.

Color Descriptors

DescriptorDescription
Dominant ColorIdentifies the most prevalent color in a region by analyzing the pixel distribution. Simplifies complex images into key color components, making it ideal for scene segmentation or coarse color analysis.
Mean Grey ValueDescribes a region using its average grey-level intensity, providing a quick statistical summary. Often used in preprocessing steps to eliminate irrelevant regions or distinguish between light and dark areas.
MPEG-7 Color DescriptorIncludes scalable histograms for variable bin sizes and non-uniform quantization. Additionally, the color structure descriptor captures spatial color patterns, offering flexibility for both global and local color representation.
Opponent Color SIFTExtends the SIFT descriptor by incorporating color information from the opponent color space, enhancing robustness to illumination changes and enabling combined spatial and color feature analysis.
Normalized RGB HistogramRedistributes pixel intensity values across RGB channels to increase contrast and highlight subtle differences in color. Particularly effective for separating visually similar areas or enhancing image contrast.
Color Constant IndexingCompares an image’s color histogram with database images, considering the area of each color region. Facilitates object recognition by matching specific color distributions with pre-stored profiles.
Color-Based Object RecognitionEnhances color-based object recognition by choosing appropriate color models and applying corrections for reflectance effects using normalized lighting conditions, improving accuracy in diverse environments.

Texture Descriptors

Local Texture Descriptors

DescriptorDescription
SIFTDetects and describes local features using gradients in a small region around detected keypoints. Highly robust to scale, rotation, and illumination changes, making it suitable for precise feature matching in dynamic environments.
ORBCombines the FAST detector and BRIEF descriptor for rapid feature detection and matching. Offers an efficient, lightweight alternative to SIFT, suitable for real-time applications and constrained computational environments.
SURFDetects features by applying a Fast Hessian Detector and computes robust descriptors using integral images. While faster than SIFT, it offers moderate robustness to scale but is less effective under rotational transformations.
RIFTExtracts rotationally invariant texture features by normalizing circular patches into concentric rings and calculating orientation gradient histograms within these rings. Ideal for texture matching under rotational changes.

Region-Based Texture Descriptors

DescriptorDescription
Gabor FiltersSimulates human visual perception by capturing texture and spatial frequency information. Gabor filters are applied in both spatial and frequency domains, providing powerful texture segmentation capabilities.
Dissociated DipolesCompares mean intensities between excitatory and inhibitory regions to highlight texture contrasts. Useful for detecting subtle differences in surface patterns or lighting conditions.
Steerable FiltersEfficiently computes orientation-specific filter responses by interpolating between predefined filter angles. Suitable for analyzing textures with directional properties, such as fingerprints or fibers.
Local Binary PatternsEncodes texture by comparing the intensity of each pixel with its neighbors, producing binary patterns. These patterns are summarized into histograms, providing a compact representation for texture classification and analysis.

Histogram-Based Texture Descriptors

DescriptorDescription
MPEG-7 Texture DescriptorEncodes texture features using histograms of edge patterns. It captures texture regularity, coarseness, and directionality, making it suitable for large-scale multimedia search and retrieval tasks.
Edge Histogram DescriptorDescribes edge patterns in an image by categorizing edges into horizontal, vertical, diagonal, and isotropic types. The spatial distribution of edges is stored in histograms for effective scene and texture analysis.

Region-Based/Global Texture Descriptors

DescriptorDescription
Co-occurrence MatricesCaptures spatial relationships between pixel intensities by computing the frequency of intensity co-occurrences. Derived statistics (e.g., contrast, energy) provide a detailed texture description.

Motion Descriptors (Repeated Section)

DescriptorDescription
Optical FlowEstimates motion between consecutive frames by analyzing pixel intensity changes. Provides a dense motion field that reflects object or observer movement, enabling applications like video stabilization and tracking.
Block MatchingDivides images into blocks and tracks the movement of these blocks between frames by searching for the best match in a predefined region. Efficient for regular motion estimation, such as in video compression algorithms.
Phase CorrelationUses phase shifts in the frequency domain to estimate translations, rotations, and scale changes between frames. This method is computationally efficient and robust to noise.
Angular Circular MotionAnalyzes motion by dividing an object into angular and circular regions, measuring pixel variations in these segments. Useful for understanding spatio-temporal object deformations and movements.