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On feature combination for multiclass object classification

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by Peter Gehler , Sebastian Nowozin
Venue:IN ICCV
Citations:259 - 5 self
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BibTeX

@INPROCEEDINGS{Gehler_onfeature,
    author = {Peter Gehler and Sebastian Nowozin},
    title = {On feature combination for multiclass object classification},
    booktitle = {IN ICCV},
    year = {},
    publisher = {}
}

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Abstract

A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture. In this paper we study several models that aim at learning the correct weighting of different features from training data. These include multiple kernel learning as well as simple baseline methods. Furthermore we derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.

Keyphrases

multiclass object classication    feature combination    multiple kernel    simple baseline method    visual object classification system    high variability    object classification datasets    multiple complementary feature    training image    boosting type method    relevant class specific aspect    intra-class variation    different aspect    benchmark datasets    visual appearance    difficult problem    several multiclass    key result    complementary feature    robust image feature    feature descriptor    key ingredient    combination method    last year substantial performance gain    correct weighting    several model    ensemble method    different feature   

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