What are local and global features in image processing?

In image processing, local features and global features are two fundamental ways to extract and represent visual information. Local features describe specific, small regions within an image, such as edges, corners, or textures. These features are often computed around key points and are designed to be invariant to changes like rotation, scale, or lighting. Global features, in contrast, summarize the entire image or large sections of it, capturing overall properties like color distribution, dominant shapes, or texture patterns. The distinction lies in scope: local features focus on details in isolated areas, while global features provide a holistic view.

Local features are particularly useful for tasks requiring precise matching or recognition of objects within varying contexts. For example, algorithms like SIFT (Scale-Invariant Feature Transform) or ORB (Oriented FAST and Rotated BRIEF) detect and describe key points by analyzing gradients or binary patterns around them. These descriptors are robust to transformations, making them ideal for applications like image stitching (e.g., creating panoramas) or object recognition (e.g., identifying a specific product in a cluttered scene). Local features excel when only parts of an image are relevant, such as detecting a logo in a photo or tracking a moving vehicle in a video sequence. Their computational complexity is higher, but they offer granularity that global features cannot match.

Global features, on the other hand, are efficient for tasks that depend on the image’s overall characteristics. A color histogram—a common global feature—quantifies the distribution of pixel colors, enabling quick comparisons for image retrieval (e.g., finding all sunset photos in a database). Texture-based features like Gabor filters or the Histogram of Oriented Gradients (HOG) summarize patterns across the entire image, which is useful for classification tasks like distinguishing between landscapes and portraits. While global features lack fine-grained detail, they are computationally lighter and work well when the image’s general properties matter more than specific details. Combining both approaches, such as using global features for initial filtering and local features for precise matching, is common in systems like content-based image retrieval or augmented reality. Choosing between them depends on the problem: local features prioritize specificity, while global features prioritize efficiency and broad context.