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International Journal of Computer Vision

Erschienen in:

01.09.2015

Generalized Dictionaries for Multiple Instance Learning

verfasst von: Ashish Shrivastava, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa

Erschienen in: International Journal of Computer Vision | Ausgabe 2-3/2015

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Abstract

We present a multi-class multiple instance learning (MIL) algorithm using the dictionary learning framework where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model and a generalized mean-based optimization framework for learning the dictionaries in the feature space. The proposed method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. Various experiments using popular vision-related MIL datasets as well as the UNBC-McMaster Pain Shoulder Archive database show that the proposed method performs significantly better than the existing methods.

Vorheriger Artikel Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment

Nächster Artikel Dictionary Learning for Fast Classification Based on Soft-thresholding

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A preliminary version of this work appeared in Shrivastava et al. (2014b). Items 2, 3 and 4 are extensions to this work.

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Titel: Generalized Dictionaries for Multiple Instance Learning
verfasst von: Ashish Shrivastava
Vishal M. Patel
Jaishanker K. Pillai
Rama Chellappa
Publikationsdatum: 01.09.2015
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2-3/2015
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-015-0831-z

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