Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition. To construct such affinity graphs is non-trivial due to the ambiguity and uncertainty inherent in the raw data. In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces for more accurately revealing the latent data distribution and thereby leading to improved data clustering, especially with heterogeneous data sources. We demonstrate the efficacy of the proposed approach on challenging image and video datasets.
The proposed affinity graph construction approach is built upon clustering random forests, with a few important merits:
An indoor dataset collected from a university campus for physical event understanding of long video streams.Details ...
C++ codes with MATLAB wrapper for unsupervised clustering forest. It can construct robust affinity graph for spectral clustering or manifold ranking. Apart from the proposed approach, the code also includes binary affinity described in A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, Springer 2013. A demo is included.