@inproceedings{258571874a8b4b06b9b9e307427240e4,
title = "Cascade of multi-level multi-instance classifiers for image annotation",
abstract = "This paper introduces a new scheme for automatic image annotation based on cascading multi-level multi-instance classifiers (CMLMI). The proposed scheme employs a hierarchy for visual feature extraction, in which the feature set includes features extracted from the whole image at the coarsest level and from the overlapping sub-regions at finer levels. Multi-instance learning (MIL) is used to learn the {"}weak classifiers{"} for these levels in a cascade manner. The underlying idea is that the coarse levels are suitable for background labels such as {"}forest{"} and {"}city{"}, while finer levels bring useful information about foreground objects like {"}tiger{"} and {"}car{"}. The cascade manner allows this scheme to incorporate {"}important{"} negative samples during the learning process, hence reducing the {"}weakly labeling{"} problem by excluding ambiguous background labels associated with the negative samples. Experiments show that the CMLMI achieve significant improvements over baseline methods as well as existing MIL-based methods.",
keywords = "Cascade algorithm, Image annotation, Multi-level feature extraction",
author = "Nguyen, {Cam Tu} and Le, {Ha Vu} and Takeshi Tokuyama",
year = "2011",
month = dec,
day = "1",
language = "English",
isbn = "9789898425799",
series = "KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",
pages = "14--23",
booktitle = "KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",
note = "International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011 ; Conference date: 26-10-2011 Through 29-10-2011",
}