Automatic Annotation and Retrieval of Images
Yuqing Song, Wei Wang, Aidong Zhang · 2002
We propose a novel approach for semantics-based image annotation and retrieval. Our approach is based on monotonic tree, a derivation of contour tree for discrete data. Monotonic tree provides a way to bridge the gap between the high-level semantics and low-level features. Each branch (subtree) of the monotonic tree is termed as a structural element if its area is within a given scale. The structural elements are classified and clustered based on their low level features such as color, spatial location, harshness, and shape. Each cluster corresponds to some semantic feature. The category keywords indicating the semantic features are automatically annotated to the images. Based on the semantic features extracted from images, high-level (semantics-based) querying and browsing of images can be achieved. The experimental results demonstrate the effectiveness of our approach.