Presentation on theme: "Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr)"— Presentation transcript:
1Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr) Tae-Kyun Kim
2Vector Clustering2D data vectors (green) are grouped to two homogenous clusters (blue and red).Clustering is achieved by an iterative algorithm (left to right). The cluster centers are marked x.
3Pixel Clustering (Image Quantisation) Image pixels are represented by 3D vectors of R,G,B values.The vectors are grouped to K=10,3,2 clusters, and represented by the mean values of the respective clusters.RGB``
4Patch Clustering (BoW in Lecture 9-10) Image patches are harvested around feature points in a large number of images.They are represented by finite dimensional vectors, and clustered to form a visual dictionary.SIFT20or raw pixelsD=40020……………dimension DK codewords…
5Image ClusteringWhole images are represented as finite dimensional vectors.Homogenous vectors are grouped together in Euclidean space.……
6K-means vs GMMTwo representative techniques are k-means and Gaussian Mixture Model (GMM).K-means assigns data points to the nearest clusters, while GMM assigns data to the Gaussian densities that best represent the data.Hard clustering: a data point is assigned only one cluster.Soft clustering: a data point is assigned multiple Gaussians probabilistically.