TY - GEN
T1 - Deriving high-level concepts using fuzzy-ID3 decision tree for image retrieval
AU - Liu, Ying
AU - Zhang, Dengsheng
AU - Lu, Guojun
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - To improve the retrieval accuracy of content-based image retrieval, an important task is to reduce the 'semantic gap' between low-level image features and the richness of human semantics. In this paper, we present a region-based image retrieval system with high-level semantic concepts used. The contribution of the paper is two-fold. First, salient low-level features are extracted from arbitrary-shaped regions. Second, a fuzzy-ID3 decision tree learning method is proposed to derive association rules which map low-level image features to high-level concepts. Experimental results prove that by reducing the 'semantic gap', the proposed system not only improves the retrieval accuracy, but also supports users in query-by-keyword. © 2005 IEEE.
AB - To improve the retrieval accuracy of content-based image retrieval, an important task is to reduce the 'semantic gap' between low-level image features and the richness of human semantics. In this paper, we present a region-based image retrieval system with high-level semantic concepts used. The contribution of the paper is two-fold. First, salient low-level features are extracted from arbitrary-shaped regions. Second, a fuzzy-ID3 decision tree learning method is proposed to derive association rules which map low-level image features to high-level concepts. Experimental results prove that by reducing the 'semantic gap', the proposed system not only improves the retrieval accuracy, but also supports users in query-by-keyword. © 2005 IEEE.
UR - https://www.scopus.com/pages/publications/33646758213
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33646758213&origin=recordpage
U2 - 10.1109/ICASSP.2005.1415451
DO - 10.1109/ICASSP.2005.1415451
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780388747
SN - 9780780388741
VL - II
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II501-II504
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -