@inproceedings{cb0b3b4e1e2b408e872fe40ed4a74531,
title = "Integrating unlabeled images for image retrieval based on relevance feedback",
abstract = "Retrieval techniques based on pure similarity metrics are often suffered from the scales of image features. An alternative approach is to learn a mapping based on queries and relevance feedback by supervised learning. However, the learning is plagued by the insufficiency of labeled training images. Different from most current research in image retrieval, this paper investigates the possibility of taking advantage of unlabeled images in the given image database to make a hybrid statistical learning feasible. Assuming a generative model of the database, the proposed approach casts image retrieval as a transductive learning problem in a probabilistic framework. Our experiments show that the proposed approach has a satisfactory performance in image retrieval applications.",
author = "Ying Wu and Qi Tian and Huang, \{Thomas S.\}",
note = "This work was supported in part by National Science Foundation Grants CDA-96-24396, IRI-96-34618 and EIA-99-75019.; 15th International Conference on Pattern Recognition, ICPR 2000 ; Conference date: 03-09-2000 Through 07-09-2000",
year = "2000",
doi = "10.1109/ICPR.2000.905268",
language = "English (US)",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--24",
booktitle = "Proceedings - 15th International Conference on Pattern Recognition, ICPR 2000 - Volume 1",
address = "United States",
}