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EnglishThis paper reports a novel approach to deal with the problem of Object and Scene recognition extending the traditional Bag of Words approach in two ways. Firstly, a dataset independent method of summarizing local features, based on multivariate Gaussian descriptors, is employed. Secondly, a recently proposed classification technique, particularly suited for high dimensional feature spaces without any dimensionality reduction step, allows to effectively exploit these features. Experiments are performed on two publicly available datasets and demonstrate the effectiveness of our approach when compared to state-of-the-art methods.
| Titolo: | Truncated Isotropic Principal Component Classifier for Image Classification |
| Autori: | |
| Data di pubblicazione: | 2014 |
| Abstract: | This paper reports a novel approach to deal with the problem of Object and Scene recognition extending the traditional Bag of Words approach in two ways. Firstly, a dataset independent method of summarizing local features, based on multivariate Gaussian descriptors, is employed. Secondly, a recently proposed classification technique, particularly suited for high dimensional feature spaces without any dimensionality reduction step, allows to effectively exploit these features. Experiments are performed on two publicly available datasets and demonstrate the effectiveness of our approach when compared to state-of-the-art methods. |
| Handle: | http://hdl.handle.net/11390/1105603 |
| ISBN: | 978-1-4799-5750-7 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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