Object classification by fusing SVMs and Gaussian mixtures

PATTERN RECOGNITION

卷:43

Article

Deselaers, Thomas^1,2  Heigold, Georg²  Ney, Hermann² 

[1] Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland

[2] Rhein Westfal TH Aachen, Human Language Technol & Pattern Recognit Grp, Aachen, Germany

关键词: Support vector machine;    Gaussian mixtures;    Discriminative classifiers;    Generative classifiers;    Local-feature-based object recognition;   

DOI  :  10.1016/j.patcog.2010.02.002

来源: Elsevier

【 摘 要 】

We present a new technique that employs support vector machines (SVMs) and Gaussian mixture densities (GMDs) to create a generative/discriminative object classification technique using local image features. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. Support vector machines are a well known discriminative classification framework but, similar to other discriminative approaches, suffer from a lack of robustness with respect to noise and overfitting. Gaussian mixtures, on the contrary, are a widely used generative technique. We present a method to directly fuse both approaches, effectively allowing to fully exploit the advantages of both. The fusion of SVMs and CMOs is done by representing SVMs in the framework of CMOs without changing the training and without changing the decision boundary. The new classifier is evaluated on the PASCAL VOC 2006 data. Additionally, we perform experiments on the USPS dataset and on four tasks from the UCI machine learning repository to obtain additional insights into the properties of the proposed approach. It is shown that for the relatively rare cases where SVMs have problems, the combined method outperforms both individual ones. (C) 2010 Elsevier Ltd. All rights reserved.

【 授权许可】

Free