2008
@article{LZL2008,
vgclass = {refpap},
author = {Ying Liu and Dengsheng Zhang and Guojun Lu},
title = {Region-based image retrieval with high-level semantics
using decision tree learning},
journal = {Pattern Recognition},
volume = {41},
number = {8},
pages = {2554--2570},
month = {August},
year = {2008},
url = {http://dx.doi.org/10.1016/j.patcog.2007.12.003},
abstract = {Semantic-based image retrieval has attracted great
interest in recent years. This paper proposes a region-based image
retrieval system with high-level semantic learning. The key features of
the system are: (1) it supports both query by keyword and query by
region of interest. The system segments an image into different regions
and extracts low-level features of each region. From these features,
high-level concepts are obtained using a proposed decision tree-based
learning algorithm named DT-ST. During retrieval, a set of images whose
semantic concept matches the query is returned. Experiments on a
standard real-world image database confirm that the proposed system
significantly improves the retrieval performance, compared with a
conventional content-based image retrieval system. (2) The proposed
decision tree induction method DT-ST for image semantic learning is
different from other decision tree induction algorithms in that it
makes use of the semantic templates to discretize continuous-valued
region features and avoids the difficult image feature discretization
problem. Furthermore, it introduces a hybrid tree simplification method
to handle the noise and tree fragmentation problems, thereby improving
the classification performance of the tree. Experimental results
indicate that DT-ST outperforms two well-established decision tree
induction algorithms ID3 and C4.5 in image semantic learning.},
}