Spatial pyramids are used in image retrieval to enhance the representation of images by capturing both local and global features. The basic idea is to divide an image into multiple regions at different scales, allowing a more detailed analysis of spatial structures within the image. Instead of treating the entire image as a single entity, the spatial pyramid approach breaks it down into several overlapping or non-overlapping sections. For instance, you can divide an image into four quadrants, then further subdivide each quadrant into smaller sections. This hierarchical structure allows the retrieval system to capture finer details in smaller regions while still considering the overall arrangement of features across the entire image.
Once the image is divided, features such as color histograms, texture descriptors, or interest points can be extracted from each region. These features are then encoded into a histogram format that represents the distribution of these characteristics across different spatial levels. For example, in a spatial pyramid with three levels, the first level might take the whole image, the second might focus on halves or quarters, and the third could analyze smaller sections like 2x2 grids or other configurations. This multi-level approach ensures that both the local patterns and broader contextual cues are incorporated into the feature representation.
During image retrieval, when a query image is processed, it undergoes the same pyramid division and feature extraction. The resulting feature vector is then compared with those in the database using similarity measures like cosine similarity or Euclidean distance. This means that the system can effectively find images that not only match in specific regions but also share similar overall layouts and compositions. Consequently, images that might be overlooked in a flat representation can be successfully retrieved, leading to more relevant results for users.