Interpolating destin features for image classification | IEEE Conference Publication | IEEE Xplore

Interpolating destin features for image classification

Publisher: IEEE

Abstract:

This paper presents a novel approach for image classification, by integrating advanced machine learning techniques and the concept of feature interpolation. In particular...View more

Abstract:

This paper presents a novel approach for image classification, by integrating advanced machine learning techniques and the concept of feature interpolation. In particular, a recently introduced learning architecture, the Deep Spatio-Temporal Inference Network (DeSTIN) [1], is employed to perform feature extraction for support vector machine (SVM) based image classification. The system is supported by use of a simple interpolation mechanism, which allows the improvement of the original low-dimensionality of feature sets to a significantly higher dimensionality with minimal computation. This in turn, improves the performance of SVM classifiers while reducing the computation otherwise required to generate directly measured features. The work is tested against the popular MNIST dataset of handwritten digits [2]. Experimental results indicate that the proposed approach is highly promising, with the integrated system generally outperforming that which makes use of pure DeSTIN as the feature extraction preprocessor to SVM classifiers.
Date of Conference: 09-11 September 2013
Date Added to IEEE Xplore: 31 October 2013
ISBN Information:
Print ISSN: 2162-7657
Publisher: IEEE
Conference Location: Guildford, UK

References

References is not available for this document.