Abstract
For the task of global-based image classification, we construct an image manifold, i.e., a pos-neg manifold, based on the solving strategies of two-class classification problem, which includes a positive sub-manifold and a negative one. We also present an improved globular neighborhood based locally linear embedding (an improved GNLLE) algorithm, fully taking account of the big differences between the positive and negative category images, thus the data distance calculation defined in the high-dimensional space can be translated into the one on the image manifold with lower dimensionality. Moreover, to simplify the distance measure between two nonlinear sub-manifolds, we put forward a clustering-based method to determine a manifold center for each sub-manifold. Experimental results on the real-world Web images show that the proposed method can improve the classification performance significantly.
Project supported by the Zhejiang Provincial Nonprofit Technology and Application Research Program of China (No. 2012C21020).
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Zhu, R., Yang, J., Li, Y., Xu, J. (2013). Constructing a Novel Pos-neg Manifold for Global-Based Image Classification. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_19
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DOI: https://doi.org/10.1007/978-3-642-53914-5_19
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