Abstract
We propose an Automatic approach for multi-object classification, which employs support vector machine (SVM) to create a discriminative object classification technique using view and illumination independent feature descriptors. Support vector machines are suffer from a lack of robustness with respect to noise and require fully labeled training data. So we propose a technique that can cope with these problems and decrease the influence of viewpoint changing or illumination changing of a scene (noise in data) named the saliency-based approach. We will combine the saliency-based descriptors and construct a Feature vector with low noise. The Proposed Automatic method is evaluated on the PASCAL VOC 2007 dataset.
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© 2012 Springer-Verlag Berlin Heidelberg
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Jalilvand, A., Moghadam Charkari, N. (2012). Multiple Object Classification Using Hybrid Saliency Based Descriptors. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_36
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DOI: https://doi.org/10.1007/978-3-642-32826-8_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32825-1
Online ISBN: 978-3-642-32826-8
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