Non-metric affinity propagation for unsupervised image categorization

Unsupervised categorization of images or image parts is often needed for image and video summarization or as a preprocessing step in supervised methods for classification, tracking and segmentation. While many metric-based techniques have been applied to this problem in the vision community, often, the most natural measures of similarity (e.g., number of matching SIFT features) between pairs of images or image parts is non-metric. Unsupervised categorization by identifying a subset of representative exemplars can be efficiently performed with the recently-proposed `affinity propagation' algorithm. In contrast to k-centers clustering, which iteratively refines an initial randomly-chosen set of exemplars, affinity propagation simultaneously considers all data points as potential exemplars and iteratively exchanges messages between data points until a good solution emerges. When applied to the Olivetti face data set using a translation-invariant non-metric similarity, affinity propa...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Delbert Dueck, Brendan J. Frey
• Keywords: Affinity Propagation, Computer Vision, ICCV 2007, Initial Randomly-chosen Set, Potential Exemplars, Representative Exemplars, Unsupervised Categorization