Realtime Hierarchical Clustering based on Boundary and Surface Statistics
This work addresses the need for fast, subconscious-like visual grouping in computer vision, which is incremental as it builds on existing clustering methods with specific improvements for real-time applications.
The paper tackles the problem of real-time hierarchical clustering for visual grouping by introducing an efficient algorithm that uses local appearance statistics and a novel cluster distance combining boundary and surface statistics, achieving state-of-the-art performance on BSDS500 and Pascal-Context datasets.
Visual grouping is a key mechanism in human scene perception. There, it belongs to the subconscious, early processing and is key prerequisite for other high level tasks such as recognition. In this paper, we introduce an efficient, realtime capable algorithm which likewise agglomerates a valuable hierarchical clustering of a scene, while using purely local appearance statistics. To speed up the processing, first we subdivide the image into meaningful, atomic segments using a fast Watershed transform. Starting from there, our rapid, agglomerative clustering algorithm prunes and maintains the connectivity graph between clusters to contain only such pairs, which directly touch in the image domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The core of this approach is our novel cluster distance: it combines boundary and surface statistics both in terms of appearance as well as spatial linkage. This yields state-of-the-art performance, as we demonstrate in conclusive experiments conducted on BSDS500 and Pascal-Context datasets.