Analyzing scenery images by monotonic tree |
| |
Authors: | Yuqing Song Aidong Zhang |
| |
Affiliation: | (1) Department of Computer and Information Science, The University of Michigan at Dearborn, 4901 Evergreen, Dearborn, Michigan 48128, USA , US |
| |
Abstract: | Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques
have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques
are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level
features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on
the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled
as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such
properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme
is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional
techniques using low-level features demonstrate the effectiveness of our approach. |
| |
Keywords: | : Content-based image retrieval – Image feature extraction – Annotation – Semantics retrieval – Monotonic tree |
本文献已被 SpringerLink 等数据库收录! |
|