TY - GEN
T1 - Learning an image manifold for retrieval
AU - He, Xiaofei
AU - Ma, Wei-Ying
AU - Zhang, Hong-Jiang
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 - 2004
Y1 - 2004
N2 - We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded in a high dimensional Euclidean space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors in Rn, the natural distance is often different from the distance induced by the ambient space R n. The geodesic distances on manifold are used to measure the similarities between images. However, when the number of data points is small, it is hard to discover the intrinsic manifold structure. Based on user interactions in a relevance feedback driven query-by-example system, the intrinsic similarities between images can be accurately estimated. We then develop an algorithmic framework to approximate the optimal mapping function by a Radial Basis Function (RBF) neural network. The semantics of a new image can be inferred by the RBF neural network. Experimental results show that our approach is effective in improving the performance of content-based image retrieval systems.
AB - We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded in a high dimensional Euclidean space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors in Rn, the natural distance is often different from the distance induced by the ambient space R n. The geodesic distances on manifold are used to measure the similarities between images. However, when the number of data points is small, it is hard to discover the intrinsic manifold structure. Based on user interactions in a relevance feedback driven query-by-example system, the intrinsic similarities between images can be accurately estimated. We then develop an algorithmic framework to approximate the optimal mapping function by a Radial Basis Function (RBF) neural network. The semantics of a new image can be inferred by the RBF neural network. Experimental results show that our approach is effective in improving the performance of content-based image retrieval systems.
KW - Dimensionality Reduction
KW - Image Retrieval
KW - Manifold Learning
KW - Riemannian Structure
KW - Semantic Space
UR - http://www.scopus.com/inward/record.url?scp=13444283482&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-13444283482&origin=recordpage
U2 - 10.1145/1027527.1027532
DO - 10.1145/1027527.1027532
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1581138938
SN - 9781581138931
T3 - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
SP - 17
EP - 23
BT - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
PB - Association for Computing Machinery
T2 - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
Y2 - 10 October 2004 through 16 October 2004
ER -