Natural scene retrieval based on graph semantic similarity for adaptive scene classification

초록

In this paper, we introduce our method for image retrieval to access and measuring the similarity of natural scenes by using graph semantic similarity. The proposed method is motivated by continuing effort from our previous work in adaptive image classification based on semantic concepts and edge detection. The method will learn the image information by concept occurrence vector of semantic concepts such as water, grass, sky and foliage. We constructed the graph using this information and illustrate the similarity with connecting edges. The empirical results demonstrated promising performance in terms of accuracy. © 2009 Springer Berlin Heidelberg.

제목
Natural scene retrieval based on graph semantic similarity for adaptive scene classification
저자
KANG SANGGIL
학회명
First International Conference on Computational Collective Intelligence - Semantic Web, Social Networks & Multiagent Systems