TY - GEN
T1 - Region-based image retrieval with high-level semantic color names
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 - Performance of traditional content-based image retrieval systems is far from users expectation due to the semantic gap between low-level visual features and the richness of human semantics. In attempt to reduce the semantic gap, this paper introduces a region-based image retrieval system with high-level semantic color names. In this system, database images are segmented into color-texture homogeneous regions. For each region, we define a color name as that used in our daily life. In the retrieval process, images containing regions of same color name as that of the query are selected as candidates. These candidate images are further ranked based on their color and texture features. In this way, the system reduces the semantic gap between numerical image features and the rich semantics in the users mind. Experimental results show that the proposed system provides promising retrieval results with few features used. © 2005 IEEE.
AB - Performance of traditional content-based image retrieval systems is far from users expectation due to the semantic gap between low-level visual features and the richness of human semantics. In attempt to reduce the semantic gap, this paper introduces a region-based image retrieval system with high-level semantic color names. In this system, database images are segmented into color-texture homogeneous regions. For each region, we define a color name as that used in our daily life. In the retrieval process, images containing regions of same color name as that of the query are selected as candidates. These candidate images are further ranked based on their color and texture features. In this way, the system reduces the semantic gap between numerical image features and the rich semantics in the users mind. Experimental results show that the proposed system provides promising retrieval results with few features used. © 2005 IEEE.
UR - https://www.scopus.com/pages/publications/84891516608
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84891516608&origin=recordpage
U2 - 10.1109/MMMC.2005.62
DO - 10.1109/MMMC.2005.62
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769521649
SN - 9780769521640
T3 - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
SP - 180
EP - 187
BT - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
T2 - 11th International Multimedia Modelling Conference, MMM 2005
Y2 - 12 January 2005 through 14 January 2005
ER -