CUED Publications database

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

Kim, T-K and Cipolla, R (2009) MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. pp. 841-848.

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Abstract

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 12:14
Last Modified: 08 Dec 2014 02:39
DOI: