Abstract
This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Multimedia and Expo |
| Publisher | IEEE Computer Society |
| Pages | I321-I324 |
| Volume | 1 |
| ISBN (Print) | 0780379659 |
| DOIs | |
| State | Published - Jan 1 2003 |
| Event | 2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States Duration: Jul 6 2003 → Jul 9 2003 |
Other
| Other | 2003 International Conference on Multimedia and Expo, ICME 2003 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 7/6/03 → 7/9/03 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
Incorporating real-valued multiple instance learning into relevance feedback for image retrieval. / Hunag, Xin; Chen, Shu Ching; Shyu, Mei-Ling.
Proceedings - IEEE International Conference on Multimedia and Expo. Vol. 1 IEEE Computer Society, 2003. p. I321-I324 1220919.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Incorporating real-valued multiple instance learning into relevance feedback for image retrieval
AU - Hunag, Xin
AU - Chen, Shu Ching
AU - Shyu, Mei-Ling
PY - 2003/1/1
Y1 - 2003/1/1
N2 - This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.
AB - This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.
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U2 - 10.1109/ICME.2003.1220919
DO - 10.1109/ICME.2003.1220919
M3 - Conference contribution
SN - 0780379659
VL - 1
SP - I321-I324
BT - Proceedings - IEEE International Conference on Multimedia and Expo
PB - IEEE Computer Society
ER -