PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Augmented attribute representations
Viktoriia Sharmanska, Novi Quadrianto and Christoph Lampert
In: 12th European Conference on Computer Vision, 7-13 Oct 2012, Firenze, Italy.

Abstract

We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:9628
Deposited By:Novi Quadrianto
Deposited On:06 December 2012