Presentation on theme: "Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr)"— Presentation transcript:
1 Lecture 3-4 Clustering (1hr) Gaussian Mixture and EM (1hr) Tae-Kyun Kim
2 Vector 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.
3 Pixel 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``
4 Patch 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…
5 Image ClusteringWhole images are represented as finite dimensional vectors.Homogenous vectors are grouped together in Euclidean space.……
6 K-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.