Image retrieval and classification using local distance functions

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Image retrieval and classification using local distance functions (2006)

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by Andrea Frome , Yoram Singer , Jitendra Malik

Venue:	Advances in Neural Information Processing Systems
Citations:	107 - 3 self

BibTeX

@INPROCEEDINGS{Frome06imageretrieval,
    author = {Andrea Frome and Yoram Singer and Jitendra Malik},
    title = {Image retrieval and classification using local distance functions},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2006}
}

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Abstract

In this paper we introduce and experiment with a framework for learning local perceptual distance functions for visual recognition. We learn a distance function for each training image as a combination of elementary distances between patch-based visual features. We apply these combined local distance functions to the tasks of image retrieval and classification of novel images. On the Caltech 101 object recognition benchmark, we achieve 60.3 % mean recognition across classes using 15 training images per class, which is better than the best published performance by Zhang, et al. 1

Keyphrases

image retrieval local distance function training image distance function object recognition benchmark patch-based visual feature novel image combined local distance function elementary distance mean recognition visual recognition local perceptual distance function

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