TY - GEN
T1 - Efficient indexing for large scale visual search
AU - Zhang, Xiao
AU - Li, Zhiwei
AU - Zhang, Lei
AU - Ma, Wei-Ying
AU - Shum, Heung-Yeung
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 - 2009
Y1 - 2009
N2 - With the popularity of "bag of visual terms" representations of images, many text indexing techniques have been applied in large-scale image retrieval systems. However, due to a fundamental difference between an image query (e.g. 1500 visual terms) and a text query (e.g. 3-5 terms), the usages of some text indexing techniques, e.g. inverted list, are misleading. In this work, we develop a novel indexing technique for this problem. The basic idea is to decompose a document-like representation of an image into two components, one for dimension reduction and the other for residual information preservation. The computing of similarity of two images can be transferred to measuring similarities of their components. The decomposition has two major merits: 1) these components have good properties which enable them to be efficiently indexed and retrieved; 2) The decomposition has better generalization ability than other dimension reduction algorithms. The decomposition can be achieved by either a graphical model or a matrix factorization approach. Theoretic analysis and extensive experiments over a 2.3 million image database show that this framework is scalable to index large scale image database to support fast and accurate visual search. ©2009 IEEE.
AB - With the popularity of "bag of visual terms" representations of images, many text indexing techniques have been applied in large-scale image retrieval systems. However, due to a fundamental difference between an image query (e.g. 1500 visual terms) and a text query (e.g. 3-5 terms), the usages of some text indexing techniques, e.g. inverted list, are misleading. In this work, we develop a novel indexing technique for this problem. The basic idea is to decompose a document-like representation of an image into two components, one for dimension reduction and the other for residual information preservation. The computing of similarity of two images can be transferred to measuring similarities of their components. The decomposition has two major merits: 1) these components have good properties which enable them to be efficiently indexed and retrieved; 2) The decomposition has better generalization ability than other dimension reduction algorithms. The decomposition can be achieved by either a graphical model or a matrix factorization approach. Theoretic analysis and extensive experiments over a 2.3 million image database show that this framework is scalable to index large scale image database to support fast and accurate visual search. ©2009 IEEE.
UR - https://www.scopus.com/pages/publications/77953213971
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77953213971&origin=recordpage
U2 - 10.1109/ICCV.2009.5459354
DO - 10.1109/ICCV.2009.5459354
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424444205
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1103
EP - 1110
BT - 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
T2 - 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -