Hyper-Spectral Image Segmentation Using Spectral Clustering With Covariance Descriptors
Conference on Image Processing - Algorithms and Systems VII, San-Jose, Kostarika, 19 - 22 Ocak 2009, cilt.7245, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 7245
- Doi Numarası: 10.1117/12.811132
- Basıldığı Şehir: San-Jose
- Basıldığı Ülke: Kostarika
- İstanbul Üniversitesi Adresli: Evet
Özet
Image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the proposed hybrid image segmentation method for hyper-spectral images.