TY - GEN
T1 - Object based image retrieval based on multi-level segmentation
AU - Xu, Y.
AU - Duygulu, P.
AU - Saber, E.
AU - Tekalp, A. M.
AU - Yarman-Vural, F. T.
N1 - Publisher Copyright:
© 2000 IEEE.
PY - 2000
Y1 - 2000
N2 - Currently, image retrieval systems are based on low level features of color, texture and shape, not on the semantic descriptions that arc common to humans, such as objects, people, and place. In order to narrow down the gap between the low level and semantic level, object-based content analysis, which segments the semantically meaningful object on images, is an essential step. In this study, we propose a learning process in order to perform effective automatic off-line analysis on a multi-level segmented image stack. Meaningful objects are extracted given certain user search patterns and interest profiles. Color and/or shape information of the objects is stored in the hierarchical content representations of the images. This information is utilized by a hierarchical matching scheme to improve the retrieval speed in the subsequent searches.
AB - Currently, image retrieval systems are based on low level features of color, texture and shape, not on the semantic descriptions that arc common to humans, such as objects, people, and place. In order to narrow down the gap between the low level and semantic level, object-based content analysis, which segments the semantically meaningful object on images, is an essential step. In this study, we propose a learning process in order to perform effective automatic off-line analysis on a multi-level segmented image stack. Meaningful objects are extracted given certain user search patterns and interest profiles. Color and/or shape information of the objects is stored in the hierarchical content representations of the images. This information is utilized by a hierarchical matching scheme to improve the retrieval speed in the subsequent searches.
UR - https://www.scopus.com/pages/publications/0033708793
U2 - 10.1109/ICASSP.2000.859229
DO - 10.1109/ICASSP.2000.859229
M3 - Conference contribution
AN - SCOPUS:0033708793
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2019
EP - 2022
BT - Image and Multidimensional Signal ProcessingMultimedia Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
Y2 - 5 June 2000 through 9 June 2000
ER -